(ETERNAL DRAFT: CONTINUALLY BEING RECONSTRUCTED)
(like life on earth)
The Meta-Morphogenesis (MM) Project (or Meta-Project?)
(Combining and extending Turing's ideas about
and his earlier ideas about discrete computation.)
Because the changes produced by the mechanisms of development and
change include modified mechanisms for producing new changes in
the mechanisms producing development and change.
Natural selection (or the biosphere) is a bit like a young child that
has begun to learn, but has no idea that is learning, what it is
learning, how it is learning, what it will do with what it has learnt,
or why it is learning. A difference is that over billions of years
natural selection modifies its information-processing abilities far
more than any child can do in a human lifetime.
The modifications include changes in physical and chemical structures
and processes (which require and also make possible more complex
information processing), changes in reproductive machinery, changes
in patterns of individual development driven by the genome
(epigenesis) both across generations and within an individual's
development, changes in forms of adaptation and learning by
individuals, changes in forms of sensing, perceiving and acting,
changes in modes of communication and control between subsystems
in an organism, changes in modes of communication and control between
organisms, changes in types of cooperative or symbiotic processing, changes
in requirements for and forms of competition, changes in abilities to
acquire and use information about other individuals, and about oneself,
changes in how parents influence offspring in their learning and
development, changes in how groups of individuals acquire use and transmit
information, changes in how societies and cultures interact, including
interactions involving new technologies, changes in the ways in which the
physical environment produces new challenges and opportunities for
information-processing in organisms of different kinds, including humans
(sometimes as a result of biological processes, or as a result of other
processes, e.g. geological events, asteroid impacts, tides, etc.) and
changes in the ways all these processes influence one another.
This is not intended to be a complete list. Extending it and
filling in details is the long term aim of the MM project.
School of Computer Science, University of Birmingham.
Offers of collaboration welcome. I have no funds for this research, and do
not intend to apply for funds. Others may do so.
The idea of a Meta-Morphogenesis project arose from a misunderstanding I
had with the editors of the award-winning book "Alan Turing: His Work and Impact"
Detailed list of contents and contributors.
2013 PROSE Award announcements
The proposal for a Meta-Morphogenesis project, partly inspired by Turing's 1952 paper
on Chemical Morphogenesis, was first presented as a chapter in that book also
available here: http://www.cs.bham.ac.uk/research/projects/cogaff/11.html#1106d
The ideas are elaborated below, and in other web pages referred to.
This version installed: 21 Oct 2012
Previous (longer) version installed: 19 Oct 2011
31 Jan 2014: added new introduction and reorganised a bit; 10 Feb 2014: Minor eds; 5 Apr 2014 (Doyle and Popper links)
12 Nov 2013 (Added section on comparison with ideas of Rodney Brooks.) ;19 Nov 2013
2 Aug 2013; 16 Aug 2013; 24 Aug 2013 (some re-formatting); 6 Sep 2013; 29 Sep 2013; 31 Oct 2013;
2 Feb 2013; 24 Apr 2013; 4 May 2013; 20 May 2013; 17 Jun 2013; (Video fixed) 24 June 2013;
6 Dec 2012 19 Dec 2012; 21 Oct 2012 (Split in two: other part here.);
10 May 2012; 22 May 2012; 19 Jun 2012; 29 Jun 2012; 7 Jul 2012; 24 Aug 2012; 13 Oct 2012; 14 Nov 2012;
20 Oct 2011; 22 Nov 2011; 21 Feb 2012 (Appendix);5 Mar 2012; 19 Mar 2012; 23 Apr 2012;
Alan Turing wrote, in a comment that is almost universally ignored, though
How could all the life, and products of life, on Earth come out of a cloud of
dust that converged to form a planet?
it occurred in one of his most widely cited papers:
"In the nervous system chemical phenomena are at least as important as electrical."
I wonder if he thought about the significance of chemistry for evolution of mind in a
in 'Computing machinery and intelligence', Mind, 59, 1950, pp. 433--460
A Protoplanetary Dust Cloud?
[NASA artist's impression of a protoplanetary disk, from WikiMedia]
Steps towards an answer
Core questions and ideas
Evolved information-processing -- in animals and machines.
(A huge, long-term multi-disciplinary project.)
How can natural selection produce minds on a lifeless planet? To fully understand our
origins we must combine familiar ideas about natural selection with ideas unavailable
to Darwin and Wallace, about evolution of information processing functions and
mechanisms, since the simplest organisms in chemical soups billions of years ago.
Many research fields can contribute, including: genetics, microbiology, ethology,
developmental psychology, neuroscience, linguistics, anthropology, philosophy of
science, philosophy of mind, computer science, Artificial Intelligence and robotics,
raising new questions about what evolution achieved and how it did so.
Explanation by natural selection is not enough
Graham Bell writes in his book Selection: The Mechanism of Evolution
Living complexity cannot be explained except through selection and does
not require any other category of explanation whatsoever.
No: adequate explanations need to mention both selection mechanisms and enabling
mechanisms, as I am sure Bell is aware.
Without enabling mechanisms, selection processes will not have a supply of new
working/viable options to choose from. In that case the selection mechanisms cannot
select new viable options.
Both the selection mechanisms and the enabling mechanisms can change during evolution
(partly by influencing each other).
There is a useful web site listing common misconceptions about evolution here:
faq.php http://evolution.berkeley.edu/evolibrary/misconceptions faq.php
However it does not bring out (or try to bring out) the full variety of types of
explanation of evolutionary phenomena. E.g. Computer systems engineers have been
discovering or inventing new types of information processing for over half a century
-- especially new types of virtual machinery. There are good reasons for thinking
that biological evolution made use of a similar discovery very much earlier, for good
reasons, some of them summarised here.
Systems biologists are constantly discovering new biological types of informed
control (information-based control). However, there may be types of biological
enabling mechanisms (e.g. forms of chemical or biological computation) that we have
not yet learnt about - and that may prevent us understanding some of the transitions
in evolution, e.g. some changes in reasoning powers in our ancestors including
changes from which we benefited.
Familiar ideas about natural selection need to be expanded to show how small changes
can build up to create increasingly complex mechanisms involved in the processes that
Point (d) involves 'recursion': evolutionary morphogenesis changes mechanisms of
new physical and chemical structures and processes supporting
reproduction, metabolism, growth, immune response, neural
new physical forms and new physical behaviours of organisms,
including new types of sensing and acting;
new information processing capabilities and mechanisms, including
sensory interpretation, motivation, learning, planning, decision
making, interrupting, self-monitoring, teaching, etc.;
new evolutionary mechanisms, including new drivers of variation
and new selection mechanisms.
evolutionary morphogenesis -- hence the label 'meta-morphogenesis'. The project
investigates how increasingly complex products of evolution produce increasingly
complex forms of information processing including new mechanisms of evolution
(generalising both ideas in Turing's 1952 paper on chemical morphogenesis and the
theory of meta-configured individual cognitive development published in
Jackie Chappell and Aaron Sloman,
Natural and artificial meta-configured altricial information-processing systems,
International Journal of Unconventional Computing, 3, 3, 2007, pp. 211--239,
which includes this diagram showing different levels at which information from
the genome and from the environment combine (after varying developmental delays):
(Chris Miall helped with the diagram)
The M-M project has begun to identify many changes in forms of biological information
processing, including transitions in mechanisms of reproduction, mechanisms of
learning and development, and inter-individual and inter-species forms of
information-processing. Examples of distinct types of transition in biological
information-processing are being collected here.
An important under-studied transition is evolution of capabilities that led to proofs
in Euclidean geometry long before modern mathematics, one of the most important
extensions of human minds in the last few millennia. How did abilities to think
philosophically evolve? Were the cognitive mechanisms unique to humans or did
unnoticed subsets develop in other species? When will our robots begin to acquire
these abilities? The questions raised in the M-M project require long term
multi-disciplinary collaborative research, perhaps comparable in scale to the Human
The idea of the M-M project was first proposed in this invited book chapter:
Virtual Machinery and Evolution of Mind (Part 3)
Meta-Morphogenesis: Evolution of Information-Processing Machinery,
in Alan Turing - His Work and Impact,
Eds. S. B. Cooper and J. van Leeuwen,
Elsevier, Amsterdam, 2013, pp. 849-856,
Additional overview materials
A 57 minute video interview at AGI 2012 in Oxford introduces some of the ideas.
With a transcript here (thanks to Dylan Holmes):
One piece of evidence that Turing might have been interested: According to his mother, he
had always been interested in living things, as depicted by her in this famous drawing:
This web site is
Also accessible as
A slightly messy PDF version is also available:
This is one of a set of documents on the meta-morphogenesis project
A draft speculative paper on the nature of mathematics and
evolution of mathematicians (Sept 2013):
And an extended abstract for a seminar on this topic on 1st Nov 2013:
"From Molecules to Mathematicians"
A partial index of a wider collection of discussion notes is in
The main concept of information used for this project
The concept of "information used by organisms or machines or biological processes
for various purposes" is central to this project. But it is not the concept unfortunately
labelled "information" by the great Claude Shannon and his many admirers. He understood
the differences but too many researchers ignore them. In fact many researchers think
that is the only concept of "information" we have. But there is a much older
The concept of information whose role in evolution, in animal perception, learning,
motivation, acting, interacting, thinking, asking, wondering, being puzzled, finding
answers (etc.) I am referring to, was already known to Jane Austen over a century
Shannon's work, and to many others long before her. Several examples from her novel
'Pride and Prejudice' published in 1813, are presented here:
Jane Austen's concept of information (As opposed to Claude Shannon's)
Readers may find it useful to try making a list of the kinds of information they use
in a typical day, and what they use those kinds for -- or, more realistically, in a
typical hour, such as the first hour after waking, including information used getting
light (if needed), deciding whether to get up, getting out of bed, getting dressed,
Further information about the Meta-Morphogenesis project:
PDF slide presentation introducing the Meta-Morphogenesis project
(Also flash version on slideshare.net.)
See also: Abstract for Meta-Morphogenesis
At: AGI 2012 -- Dec 11th Oxford
St Anne's College Oxford
A growing collection of related papers and discussion notes:
Return to list of contents
Meta-Morphogenesis: Evolution and Development of Information-Processing Machinery
(Including (recursively) mechanisms for changing the mechanisms)
The universe is made up of matter, energy and information,
each other and producing new kinds of matter, energy, information and
How? How did all this come out of a cloud of dust?
In order to find explanations we first need much better descriptions of
what needs to be explained.
This is a multi-disciplinary project attempting to describe and explain the variety
of biological information-processing mechanisms involved in the production of
biological information-processing mechanisms, on many time scales, between the
earliest days of the planet with no life, only physical and chemical structures,
including volcanic eruptions, asteroid impacts, solar and stellar radiation, and
many other physical/chemical processes (or perhaps starting even earlier, when
there was only a dust cloud in this part of the solar system?).
Evolution can be thought of as a (blind) Theorem Prover
(or theorem discoverer).
Proving (discovering) theorems about what is possible
(possible types of information, possible types of information-processing, possible
uses of information-processing)
- Proving (discovering) many theorems in parallel
(including especially theorems about new types of information and new useful types of
Sharing partial results among proofs of different things
(Very different biological phenomena may share origins, mechanisms, information, ...)
Combining separately derived old theorems in constructions of new proofs
(One way of thinking about symbiogenesis.)
Delegating some theorem-discovery to neonates and toddlers (epigenesis/ontogenesis).
(Including individuals too under-developed to know what they are discovering.)
Delegating some theorem-discovery to social/cultural developments.
(Including memes and other discoveries shared unwittingly within and between
Using older products to speed up discovery of new ones
(Using old and new kinds of architectures, sensori-motor morphologies, types of
information, types of processing mechanism, types of control & decision making, types
The "proofs" of discovered possibilities are implicit in evolutionary and/or developmental
The proofs demonstrate the possibility of
development of new forms of development
evolution of new types of evolution
learning new ways to learn
evolution of new types of learning
(including mathematical learning: by working things out
without requiring empirical evidence)
evolution of new forms of development
development of new forms of learning
(why can't a toddler learn quantum mechanics?)
how new forms of learning support new forms of evolution
how new forms of development support new forms of evolution
(e.g. postponing sexual maturity until mate-selection mating
and nurturing can be influenced by much learning)
.... and ways in which social cultural evolution add to the mix
These processes produce new forms of representation, new ontologies and information
contents, new information-processing mechanisms, new sensory-motor morphologies, new
forms of control, new forms of social interaction, new forms of creativity, ... and
more. Some may even accelerate evolution.
A growing list of transitions in types of biological information-processing:
Biology, Mathematics, Philosophy, and Evolution of Information Processing
Mathematics is at root a biological, not an anthropological, phenomenon
(as suggested by Wittgenstein).
But its possibility depends on deep features of the universe, some of which
evolution had to 'discover':
attempt to identify a major type of mathematical reasoning with precursors in
perception and reasoning about affordances, not yet replicated in AI systems:
Even in microbes
I suspect there's much still to be learnt about the varying challenges and opportunities
faced by microbes at various stages in their evolution, including new challenges produced
by environmental changes and new opportunities (e.g. for control) produced by previous
evolved features and competences -- and the mechanisms that evolved in response to those
challenges and opportunities.
Example: which organisms were first able to learn about an enduring spatial configuration
of resources, obstacles and dangers, only a tiny fragment of which can be sensed at any
What changes occurred to meet that need?
Use of "external memories" (e.g. stigmergy)
Use of "internal memories" (various kinds of "cognitive maps")
More examples to be collected here:
of some of these ideas on 5th Jun 2012, after reading
a draft workshop paper on Meta-morphogenesis and the Creativity of Evolution:
For a (very) compressed history of information processing on our
Evolution, Life and Mind: Some Startling Facts
For a messy, still growing, collection of examples relating to learning and development
see this web page on "Toddler theorems":
(including an introduction to the idea of a "Domain").
Related talks (PDF) can be found here:
What is Meta-Morphogenesis?
Draft answer (last revised: Aug 2013):
The study of meta-morphogenesis (MM) is the study of
Forms of natural information processing, including perception, learning,
inference, control, explanation, prediction, communication, ...
The structures that can be used for these purposes
(including physical structures and abstract, or virtual-machine structures,
discrete and continuous structures, static structures, dynamic structures, ...,
structures within organisms and structures in the environment),
Meta-Morphogenesis: of virtual machinery with "physically indefinable" functions
(Slides for presentation given at the Workshop "The Incomputable" (superseded)
Royal Society Kavli Centre: 11-15 June 2012)
Mechanisms involved in such forms of information processing
Mechanisms for producing or modifying such mechanisms, including these
Examples of meta-morphogenesis include the evolution of evolvability, the
evolution (across generation) of new mechanisms for development and learning and
the development of new mechanisms of development and learning.
Often the changes occur in parallel streams of mutual influence of different
forms of change or development (e.g. "arms races").
can happen both in co-evolution, in co-development in different individuals and
in co-development of different subsystems within an individual.
Many of the developments make essential use of virtual machinery
The more recent products of meta-morphogenesis include
forms of representation, mechanisms and architectures providing abilities to
represent not only what is the case, but also possibilities, and
constraints on possibilities (including many varieties of affordance)
forms of reasoning about what is possible and what is necessarily
the case, which explains why mathematical interests and capabilities are
forms of representation, mechanisms and architectures providing meta-semantic
competences (including meta-management)
the phenomena referred to by Annette Karmiloff-Smith as
"Representational Redescription", discussed in
Types of transition in which two or more different sub-systems begin to
cooperate to provide previously impossible functions include introduction of
abilities to scale-out (as opposed to scaling up) as discussed in
Abstract for talk about meta-morphogenesis in Cambridge, 8th May 2012:
Presentation By Penrose, Manchester 2012
At the Alan Turing centenary conference in Manchester (June 2012)
Added 12 Aug 2012
Roger Penrose seems to partially agree with one of the ideas here
Roger Penrose gave the final keynote lecture, which was open to the public. His
lecture (The Problem of Modelling the Mathematical Mind) was recorded on video
and is now available online:
Questions from the audience were also recorded. Near the end of the video (at
approximately 1 hour 26 minutes from the start) I had a chance to suggest that
what he was trying to say about human consciousness and its role in mathematical
discovery might be expressed (perhaps more clearly) in terms of the kinds of
meta-cognitive functions required in animals, children, and future robots, as
well as mathematicians. The common process is first gaining expertise in some
domain (or micro-domain!) of experience and then using meta-cognitive mechanisms
that inspect the knowledge acquired so far and discover the possibility of
reorganising the information gained into a deeper, more powerful, generative
form. The best known example of this sort of transition is the transition in
human language development to use of a generative syntax. (At one point I
mistakenly referred to a "generative theorem" when I meant "generative theory".)
I suggested that something similar must have happened when early humans made the
discoveries, without the aid of mathematics teachers, that provided the basis of
Euclidean geometry (later systematised through social processes). I have
proposed that there are many examples, that have mostly gone unnoticed, of young
children discovering what I call "Toddler theorems", some of them probably also
discovered by other animals, as discussed in
This is also related to the ideas about "Representational Re-description" in the
work of Annette Karmiloff-Smith, presented in her 1992 book Beyond Modularity
Penrose seemed to agree with my suggestion, and to accept that it might also
explain why the basis of some mathematical competences are biologically
valuable, which he had previously said he was doubtful about. I don't know
whether he realised he was agreeing to a proposal that instead of thinking of
consciousness as part of the explanation of human mathematics, we can switch to
thinking of the biological requirement for mathematical thinking as part of the
explanation of important kinds of human (and animal) consciousness.
This is also connected with the need to extend J.J.Gibson's theory
of perception of affordances discussed in
Return to list of contents
PAPERS WITH FURTHER DETAILS
EXISTING PAPERS AND PRESENTATIONS
Example papers and presentations I have written on this topic over the last five
decades (DPhil Thesis was in 1962), especially since the early 1990s.
(Currently this list duplicates the list in the Toddler theorems paper.)
PAPERS ON META-MORPHOGENESIS
RELEVANT PRESENTATIONS (PDF)
CLOSELY RELATED (To be expanded and re-ordered)
Richard Dawkins, 'The Evolution of Evolvability',
Artificial Life: Proceedings of an Interdisciplinary Workshop on the Synthesis
and Simulation of Living Systems,
Ed. Chris G. Langton, Addison-Wesley, 1988, pp. 201--220.
Dawkins' paper is entirely about evolution of physical form, and of procedures
for producing physical forms. The idea of meta-morphogenesis includes evolution
of behaviours, evolution of information processing (including mechanisms for
producing and controlling behaviour), evolution of forms of learning, learning,
evolution of mechanisms of development of new information-processing
capabilities, evolution of abilities to alter the evolvability of all of those.
Dawkins paper is a useful introduction to the basic idea, with informative toy
Popper's Darwin Lecture: (Linked here 5 Apr 2014)
Natural Selection and the Emergence of Mind
Delivered at Darwin College, Cambridge, November 8, 1977
Kindly made available on Bob Doyle's remarkable web site which is full of relevant
(I learnt about this for the first time on 4th Apr 2014. Google should have
introduced us sooner!)
Beyond Modularity, by Annette Karmiloff-Smith MIT
- Kenneth Craik's 1943 book (The Nature of Explanation), written nearly
70 years ago makes some major contributions to the meta-morphogenesis project by
drawing attention to previously unnoticed problems about biological information
processing in intelligent animals.
For a draft incomplete discussion of his contribution, see
Natural and artificial meta-configured altricial information-processing systems
Jackie Chappell and Aaron Sloman
International Journal of Unconventional Computing, 3, 3, 2007, pp. 211--239,
The full variety of powerful information-processing mechanisms 'discovered' by
evolution has not yet been re-discovered by scientists and engineers. By
attending closely to the diversity of biological phenomena, we may gain new
(a) how evolution happens,
(b) what sorts of mechanisms, forms of representation, types of learning
and development and types of architectures have evolved,
(c) how to explain ill-understood aspects of human and animal intelligence,
(d) new useful mechanisms for artificial systems.
We analyse trade-offs common to both biological evolution and engineering
design, and propose a kind of architecture that grows itself, using, among other
things, genetically determined meta-competences that deploy powerful symbolic
mechanisms to achieve various kinds of discontinuous learning, often through
play and exploration, including development of an 'exosomatic' ontology,
referring to things in the environment - in contrast with learning systems that
discover only sensorimotor contingencies or adaptive mechanisms that make only
minor modifications within a fixed architecture.
Margaret Boden's work
There is much relevant content in Margaret Boden's work, e.g. on purposive
explanation in psychology, on achievements and limitations of AI, on creativity,
her theoretical work on biology (especially the relations between life and mind)
and her outstanding historical analyses of various aspects of the development of
Mind As Machine: A history of Cognitive Science (Vols 1--2) (2006)
Rodney Brooks on layered architectures and evolution
In the mid 1980s, after apparently becoming dissatisfied with the state of AI, Brooks
wrote a series of very influential papers that recommended a 'layered' approach to AI
design, namely build systems that have relatively simple capabilities and then add
new more sophisticated capabilities, that run in parallel with and make use of the
older capabilities. He also related this to suggestions about biological evolution
and the relative information-processing complexity of evolutionarily very old
organisms, suggesting that what was added more recently to provide human functionality
added relatively little. His ideas overlap with (and probably helped to influence)
those presented in this project, but there are also deep differences. E.g. I don't
claim that we can start building simple organisms with our current technology that
provide suitable old, 'lower level' layers on which to add newer, more sophisticated,
layers of competence. I am suggesting that what is old in the evolutionary history of
existing organisms may have many unobvious features that the M-M project should
attempt to uncover.
I also don't propose that it will suffice to start from multi-cellular organisms like
insects, that have already evolved capacities to move around in rich and complex
environments, foraging, feeding, mating, building nests, etc. Instead I consider the
possibility that even at the single-celled level there may have been forms of
information processing that underpin some of the types of information processing that
interest us in humans and other animals.
Brooks' suggestion that the importance of internal representations has been
over-rated because the best representation of the world is the world itself, has been
highly influential, but is at most relevant to what I've called 'online intelligence'
involved in control of movements and manipulations using feedback mechanisms of
various sorts. (H.A.Simon made similar points.) For deliberative and meta-semantic
competences the slogan is not merely wrong: it has been positively harmful.
Also the ideas in the CogAff project and the CogAff architecture schema allow for a
richer variety of types of architecture than the type of layered subsumption
architecture proposed by Brooks, though it's possible that each could be modified to
cover more of the features of the other.
His work had enormous influence in many research and teaching centres. Unfortunately
the people influenced were often much less intelligent and less subtle than Brooks,
and as a result much of the influence has been bad. Hence my critique.
David Kirsh wrote a critical review of Brooks' ideas around 1986, published in 1991
(ref below). Brooks wrote a reply, cited below, published in 1997. I wrote a somewhat
different critical commentary much later, partly based on the unpublished note on
requirements, cited below.
David Kirsh, Today the earwig, tomorrow man?, in
Artificial Intelligence, 47, 1, 1991, pp. 161--184,
Rodney A. Brooks, From earwigs to humans, in
Robotics and Autonomous Systems, 20, 1997, pp. 291 - 304
Some Requirements for Human-like Robots:
Why the recent over-emphasis on embodiment has held up progress, in
Creating Brain-like Intelligence,
Eds. B. Sendhoff, E. Koerner, O. Sporns, H. Ritter and K. Doya, Springer-Verlag, 2009, pp. 248--277,
Requirements for a Fully Deliberative Architecture (Or component of an architecture),
Unpublished Research Note, COSY-DP-0604,
School of Computer Science, University of Birmingham, UK, May, 2006,
Brian Goodwin, whom I met and talked to occasionally at Sussex University
expressed ideas in conversation (and in his publications which I did not read,
mainly because I could not keep up with the mathematical details), had ideas
about natural selection being only part of the story of how evolution works: he
used to talk about "Laws of Form" constraining the possibilities for growth in
ways that did not require genetic control. In retrospect I think some of the
ideas behind the M-M project may have come from him, and before him from D'Arcy
Thompson, Goethe and others. See Boden (2006)
Sections 15x(b-d), Vol 2
However, some of the "laws of form" are concerned with forms of information
processing and how possibilities are enabled and constrained by (a) the physical
mechanisms in which the information processing machinery (even virtual
machinery) has to be implemented and (b) the environments with which organisms
need to interact in order to develop, learn, live their lives and reproduce --
some of which include other information processors: friends, foes, food,
playmates, and things to observe or be observed by.
- Stuart Kauffman's work, e.g. see this useful overview by Gert Korthof
Kauffman's 1995 book is very approachable:
At home in the universe: The search for laws of complexity
- Ideas of David Deutsch. See his old and new web sites:
(Not working when I last looked)
- Two books by Jack Cohen (biologist) and Ian Stewart (mathematician)
The Collapse of Chaos (1994)
Figments of Reality: The Evolution of the Curious Mind (1997)
- Immanuel Kant's Critique of Pure Reason (1781)
has relevant ideas and questions, but he lacked our present understanding of
information processing (which is still too limited)
- Much of Jean Piaget's work is also relevant, especially his last two
(closely related) books written with his collaborators:
Possibility and Necessity
Vol 1. The role of possibility in cognitive development (1981)
Vol 2. The role of necessity in cognitive development (1983)
Tr. by Helga Feider from French in 1987
Like Kant, he had deep observations but lacked an understanding of information
processing mechanisms, required for explanatory theories.
- John McCarthy's 1996 paper "The Well Designed Child" is very relevant:
(Later published in the AI Journal, 172, 18, pp 2003--2014, 2008)
"Evolution solved a different problem than that of starting a baby with no a
"Animal behavior, including human intelligence, evolved to survive and succeed
in this complex, partially observable and very slightly controllable world. The
main features of this world have existed for several billion years and should not
have to be learned anew by each person or animal."
- Ulric Neisser wrote in Cognition and Reality, W.H. Freeman., 1976.
"... we may have been lavishing too much effort on hypothetical models of the
mind and not enough on analyzing the environment that the mind has been shaped
- Steve Burbeck's web site:
- Daniel Dennett's very readable little book is very relevant:
Kinds of minds: towards an understanding of consciousness,
Weidenfeld and Nicholson, London, 1996,
This book, like much of what Dennett has written is mostly consistent with my
own emphasis on the need to understand "the space of possible minds" if we wish
to understand human minds. Simply trying to study human minds while ignoring all
others is as misguided as trying to do chemistry by studying one complex
molecule (e.g. haemoglobin) and ignoring all others.
Dennett and I have also written similar things about how to think about
discussions of "free will" in the light of changes produced by Biological
Elbow Room: the varieties of free will worth wanting,
Oxford: The Clarendon Press, 1984,
(See also his later book Freedom Evolves)
A. Sloman, 'How to Dispose of the Free-Will Issue,'
In AISB Quarterly, No 82, 1992, pp. 31--32,
(Originally posted to Usenet some time earlier.)
Also used (with my permission) as the basis for Chapter 2 of
Artificial Minds, MIT Press, 1995,
(Franklin expanded my notes.)
Our main difference is that I don't regard what Dennett calls "the intentional
stance" as a requirement for a science of mind, since reference to mental states
and processes is not merely a sort of useful explanatory fiction: those states
and processes, and qualia exist and their existence can be explained in terms of
the operation of virtual machinery that is a product of biological evolution
rather than human engineering. However, Dennett sometimes also seems to
hold that view.
Noam Chomsky's early work deeply influenced my thinking, especially the idea of
generative forms of representation able to cope with arbitrary
(essentially infinite) variation in structure (not just values of a fixed size
vector, so popular in much current AI). See his three notions of 'adequacy',
observational, descriptive and explanatory adequacy, in Aspects of the theory
of syntax (1965)
Merlin Donald's book
A Mind So Rare: The Evolution of Human Consciousness (1992)
Is very relevant. It is spoilt especially near the beginning, by excessive rants
against reductionism, which originally put me off reading the rest of the book. So it
lay in a pile of books to be read for several years before I returned to it. Despite
the complaints about reductionism much of the book attempts to relate empirical
claims about the capabilities of humans and other animals to requirements for
explanatory information processing mechanisms. The author does not seem to be well
informed about achievements of AI and the nature of symbolic computation, so his
sketchy ideas about explanatory mechanisms can mostly be ignored. But the book gives
a superb introduction to many of the evolutionary transitions that involve
information-processing, e.g. Chapter 4.
In particular, much of what Merlin Donald has written about evolution of consciousness
is relevant to this project, though it is not clear that he appreciates the
importance of virtual machinery, as outlined in
How Virtual Machinery Can Bridge the ``Explanatory Gap'', In Natural and Artificial Systems,
Invited talk at SAB 2010, Paris, in
Proc. SAB 2010, LNAI 6226, Eds. S. Doncieux and et al., Springer, 2010, pp. 13--24,
The Only Way is Up
On A Tower of Abstractions for Biology
Jasmin Fisher, Nir Piterman, and Moshe Y. Vardi
17th International Symposium on Formal Methods, LNCS 6664, pp. 3-11,
We draw an analogy between biology and computer hardware systems and argue for
the need of a tower of abstractions to tame complexity of living systems. Just
like in hardware design, where engineers use a tower of abstractions to produce
the most complex man-made systems, we stress that in reverse engineering of
biological systems; only by using a tower of abstractions we would be able to
understand the "program of life".
Others -- to be added
I know there are lots more related books and papers -- most of them not yet read by
me. I would welcome a volunteer collaborator (or a group of collaborators) to help
setting up an annotated online bibliography of notes, books, papers, discussions,
videos, etc. relevant to meta-morphogenesis, whether the label is used or not,
especially freely available open access documents, for reasons given here.
Appendix: Schematic Summary
Transitions can occur in parts of organisms, in whole organisms, within a species, in
interacting groups of species, in societies, and in environments (though organisms
are part of the environment for conspecifics and for others).
A sample list of types of transition produced by biological mechanisms
The mechanisms include evolution by natural selection, individual learning, cultural
development and transmission, including changes in genomes as well as changes in
factors affecting gene expression.
Note added 23 Oct 2012
An expanded version of the above list of transitions is being created in
Change of physical shape (in individual, in species)
Change in physical behaviour (in individual, in species)
Change in information processing (in individual, in species)
(including control of growth, metabolism, immune system, processing of
perception, motive formation, motive selection, action selection, action
control, learning, reasoning, ...)
Change in developmental trajectory (physical, non-physical)
Change in what can be learnt (in individual, in species)
Change in type of interaction between individuals
(in same species, across species, within `family unit', prey,
Change in type of social organisation
(including forms of collaboration, forms of nurturing, forms of
education, forms of competition)
Changes in mechanisms of evolution (evolution of evolvability
Changes in mechanisms of development
Changes in mechanisms of learning
Changes in mechanisms of interaction
Changes in mechanisms of self-monitoring, self-control
Introduction of new virtual machines, new forms of representation, new
ontologies, new architectures
These changes can interact and influence one another...
Types of Meta-Morphogenesis:
For any of the above biological changes B1, B2, B3,.. etc. and for
any environmental states or changes E1, E2, E3,... there can be influences
of the following forms ...
Things that cause changes can produce new things that cause changes.
Old phenomena may be produced in new ways
e.g. information acquired and ways of acquiring and using
information can change.
Often new mechanisms can produce new biological phenomena
e.g. organisms that can discover what they have learnt.
In particular, most forms of biological information processing that exist now are
organisms that make and use mathematical discoveries.
products of parallel trajectories of biological information processing over many
stages of evolution and development, including cultural evolution in the case of
This is quite unlike use of evolutionary computation (GA, GP, etc.) with a fixed
evaluation function, often used to solve engineering problems.
For example, evaluation in natural evolution keeps changing, as environments,
including competitors, prey, symbionts, diseases, etc. change.
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School of Computer Science
The University of Birmingham