The Tale of Two Rivers - Humans assessing Risk

Because of the storms affecting public transport on Christmas Eve I drove to Bookham, Surrey, to collect someone who had been planning to spend Christmas with us, and who could not come by rail because of the disruption. The journey South onto the M25 and round to Leatherhead was remarkably fast and while the journey back was slower most of the delay was cause by an accident on the M3 which caused a very long queue to form on the M25. On safely returning home I turned on the news and two rivers were mention by name because of the serious effects of the storm.

The first was the River Lemon, at Newton Abbot, Devon, which I know from my childhood days. There had been an unfortunate incident when a dog had fallen into the flooded river and its owner had gone in to rescue it and had drowned. As seems usual in such very sad cases, the dog succeeded in climbing out further down stream. The problem is that in such situations there are a number of possible outcomes and people are bad at weighing up the risk, particularly when the desired outcome is highly probable but one of the unwanted outcomes is highly undesirable, as in this case.

The other river mention made me realise that I am also bad at accessing risks when there is a very high cost of making the wrong decision compared with the possible benefit of being right.

In my journey home I crossed the River Mole, which runs between Bookham and Leatherhead.

Flood Warning Area 25th December

On approaching Cobham from the Effingham direction the traffic halted. There was water on the road flowing from right to left for a distance of about 50 yards. One car was stationary of the left hand side and appeared to be abandoned. Several cars in front of us turned round in the road and retreated in the direction from which they had come.  However two of the larger cars decided to go through on the right hand side of the road and I watched them very carefully. In my younger days I had learnt to drive in a rural area where there were a number of fords.  I reckoned that it would be safe if the water was no deeper than the rim of the tyres. As far as I could see the water was almost exactly at that level so I went ahead and got through safely - saving perhaps 15 minutes if I had gone back and found a different route.

Of course things worked out as planned - but if things had gone wrong and the car had stalled the problems, with two old age pensioners have to abandon the car could have been at best very traumatic, especially as I now know that the river levels were still rising...  OK - this time my luck held - but at my age (and with an elderly passenger) I need to to consider the costs of being in error more carefully.

Rural Relaxation - A Winter Afternoon at College Lake

Winter Sunlight gives a warm glow to College Lake

Because I have been busy on other things I have rather neglected this blog recently - but I still find time to get out into the countryside. This picture was taken yesterday at the College Lake Nature Reserve, which is only a few miles from where I like. It is an ideal place to relax as there is always plenty of wild life to see, and (until an hour before the site closes) a small cafe where you can get a hot drink after a bracing walk.

More Pictures of College Lake 

The more we probe the brain, the less we understand it

The New Scientist of 19th October has an article "Hidden depths - The vast majority of brain research is now drowning in uncertainty. It is time to build a more complete understanding of the mind" by Ingfei Chen. This queries the foundations of much of the research into scans which attempt to relate what we are thinking with neural activity. The approach seems to be the more and more detail we have the better we can understand - but that is true only if we are asking the right question. Readers of this blog will know I believe that we are already drowning in detail without understanding the neural code. If the neurons use the same code, whatever their detailed task,  you don't need to look at millions - you need to stand back and look for the common features. As a result I have submitted the following letter to the New Scientist, and also posted it as a comment to the article.

Brain research is drowning in uncertainty (Hidden Depths, New Scientist 19^th October) because nearly everyone is looking for a non-existent Philosopher's Stone of Biological Human Intelligence. In reality we have the same unsophisticated neural code as animals but have replaced a degree of critical thinking with a sheep-like “follow my leader” approach to speed learning. Of course this is an advantage as culturally learnt intelligence skills, communicated by language, are very much more powerful than our genetically based animal intelligence. Unfortunately the inherent weaknesses of the comparatively crude biological foundations show through as confirmation bias and the way our memories of past events drift with time. In evolutionary terms our big brain is no more significant than a giraffe's long neck, and we might understand how the brain works better if we were not so “big-headed” as to think our brain is biologically anything special.

Do Scientists "Believe" in Evolution?

I was most interested to see Glenn Branch's blog post What's wrong with 'belief in Evolution? and posted the following comment: 

As someone who is interested in the role of the brain's neural code in the evolution of humans I don't see any differences in the way the creationist and the scientist's brains work at the biological level. The basic processing mechanism favours confirmation bias – where new information that reinforces what you already know is readily accepted, while negative information is either “not seen” or deliberately ignored. Everyone has their own personal model of the world, and the rules by which new information is acceptable. Our intelligence is because we learn rapidly, using language, because we naturally believe what we are told! A belief in the value of the scientific method opens up a universe of practical knowledge on which our civilization depends, and I find such a belief system extremely satisfactory because it works.

However the brain is capable of working with other belief systems and many people find it more satisfactory to be in a supportive community which is (in my terms, but not in theirs) detached from reality. In effect out intelligence arises from our ability to learn much faster than animals (due to our language) but this has the disadvantage that there is a natural tendency to follow charismic leaders.

In evolutionary biological terms we all believe, without question, most of what we learn as children. And scientist and creationist brains work in the same way, trusting information from people who have similar belief systems to their own – and rejecting belief systems they find unacceptable.

If you assume the kind of brain model I describe on this blog we all "believe" without question what we have learnt throughout life - and we would be less mentally efficient is we spent time checking up on the validity of what we are told. We should no assume that the way a scientist justifies science is any different - in terms of biological activity at the neuron level - than the way a religious person justifies religion.

From Neural Code to Religion - An Evolutionary Model of the Human Brain

A New Look at the Evolution of the Human Brain

A talk given to the Chiltern Humanists on 10th October, 2013

Copy of slides (pdf)

Illustrated text supporting the slides (pdf)

The following notes outline the arguments which underlie the talk. Most technical information on the model of the neural code used, and the related CODIL research, are available on this blog, and I am happy to answer any questions/comments about more technical aspects of the research.

Evolution of the Human Brain

Chiltern Humanist Meeting

Evolution of the Human Brain

The first of our autumn series of meetings will be held on Tuesday 10 September at Wendover Library.

Chris Reynolds, a retired scientist who has been a member of our group for several years, will take a new look at the evolution of the human brain. This has been researched at a biological level and raises the question of whether there is an inbuilt reason why some people are drawn to religion whilst others are not.

By temperament Chris likes to stand back and get an overview, rather than getting stuck in a narrow specialist area. After taking a doctorate in Chemistry he started working with computers in 1965; he was soon involved in research, and developed a language called CODIL over the following years. As Reader in Computer Science at Brunel University, Uxbridge during the 1970’s, he became involved in a project, funded by the British Library, concerned with interactive publication, which in a very elementary way anticipated the World Wide Web. Later he edited an online professional book review service on the subject of Human-Computer Interaction. In retirement his main interests are genealogy and local history.

His talk promises to be an interesting and different take on evolution.

Humans like to think they are something special - If not actually made by God in his own image, or the centre of the universe, are least we can console ourselves that we are more intelligent than the other animals that inhabit  our planet.

Or can we? No animal needs a brain that is bigger than necessary to survive, and we only have to look at the other mammals that share this planet to see that there are many cases where a species can be characterized by a greatly enlarged organ, whether it is a giraffe with its long neck, an elephant with its greatly extended nose, or the hands of the bat. And what about the changes we see in the whales!

This talk assumes that all mammals have brains that use the same neural code, and that the human brain is no more than a normal animal brain which has been supercharged to give it more processing capacity. It considers the limitations one might expect from a very simple neural code, and asks what the evolutionary pressures would be on the braians of hominids who were faced with the drying out of the African rain forests three million years ago.

The key factor would seem to be the point where cultural knowledge passed between the generations became more important to survival than the basic brain mechanisms on their own. At this point it there was an advantage in have a larger brain and developing faster mechanisms for learning. Better learning means better tools for survival, and one of those tools is language, which will automatically develop from generation to generation. One could get an auto-catalytic situation where the culture we pass on is augmented at a growing rate in each successive generation. 

Unfortunately the basic animal neural code is mathematically not very sophisticated, and while this is not important to other animals the defects become more evident as the human species pushes the code to its limits. While many of the defects can be avoided using language the logical weaknesses, such as confirmation bias, can, and are, exploited by religions and political belief systems. Even scientists will not be immune, as they take part in the rat race for prestige and funds!

After the talk I will be posting the slides used and background notes on this blog..

A Poem for Today

Way down South where bananas grow

A grasshopper stepped on an elephant's toe

The Elephant said, with tears in his eyes

"Pick on somebody your own size."

Not a usual post for this site - but I feel a poem which rattles the walls of one's mental box is worth a mention. It is the poem for the 27th August in Read Me - A Poem A Day for the National Year of Reading, chosen by Gaby Morgan (Macmillan, 1998).

How Much is a Human Life Worth?

Ian Brady

Placing a financial value on human life is a task which most people avoid – but society's resources are limited, and so are those of the world. By failing to discuss the issue decisions are taken which by implication place a value on human life or which have unintended financial consequences.

Let us look at an simple example. The Moors Murderer, Ian Brady, wants to die. He recently asked if he could be moved from a psychiatric hospital – where he can be forcibly feed to keep him alive against his will – to a prison – where he would be able to starve himself to death. The decision was that he should be kept alive, force fed when appropriate, until he died in prison of natural causes.

When I was young Ian Brady would have been hung but it was decided that the punishment of being left to rot in prison till you die of old age should be substituted. In Ian Brady's case the decision not to hang him means that society will end up spending something approaching £3,000,000 keeping him alive for perhaps 60 or more years in a high security establishment. (This figure excludes the £450,000 costs of the recent legal hearing). This sum would be sufficient to build a small new primary school, to fund a consultant doctor for many years, or to provide enough clean water in Africa to save the lives of hundreds of children.

So one of the unintended consequences of our laws, and the way they are implemented, means that preserving the life of a multiple child murderer (who himself considered the lives of the children he killed valueless) is worth more (in terms of tax-payers' money) than saving the lives of hundreds of innocent African children.

Of course, you will say, we got rid of hanging because sometimes an innocent person was hanged. Of course this will happen occasionally, and it is very sad. Mistakes occur in every area of life, and always will, whatever laws we have. Innocent people are dying unnecessarily every day for all sorts of different reasons and in every case it is very sad. If we spend enormous sums of money to avoid accidentally killing one innocent person this means resources are not available to save the lives of many other innocent people. 

Lucy

Belinda

Let us put this into a personal context. At my daughter Lucy's inquest the coroner indirectly suggested that if she had been recognised as being desperately mentally ill when she was in a police station she might not have killed herself on an anniversary associated with the misdiagnosis. At my daughter Belinda's inquest the coroner actually added a formal rider of neglect concerning her National Health Service treatment. In both cases the way that the criminal law treated the mentally ill was an issue. If  society had decided to spend less money on prisons and more on the mentally ill perhaps both my daughters would still be alive.

~~~~~~~~~~~~~~~~~~~~~

The above is a slightly edited version of part of a text I prepared in connection with a Tring U3A debate on "How Much is a Human Life Worth?" 

Rural Relaxation - The Creation of College Lake Nature Reserve

My daughter Lucy's tragic death in 1985 had a major effect on my life, and was undoubtedly an important factor in my abandoning my research and taking early retirement. As a result I spent some 20 years on voluntary work to try and improve the lot of the mentally ill - and I urgently needed somewhere to relax

And I found just the place - a new reserve was being set up only a few miles from from where I live - called College Lake. When I first visited it there was a large white hole in the ground with the promise of a nature reserve at the south end - and a working chalk quarry at the north end. Graham was often seen with an enthusiastic team of volunteers, while Rita's little Sunday cafe in the old barn was not to be missed.

What is so hard about research?

Jody Passanisi and Shara Peters have recently posted a guest blog on the Scientific American entitled What is so hard about research? which looks at the problems of getting students to think imaginatively about research and the available research tools, and commenting that they tend to look for quick and easy options rather than stop and think about the problem. In reply I have posted the following comment.

Do Prairie Dogs have a sophisticated communication language

In trying to model the neural code in human and animal brains one of the key factors to be considered is that of language and whether the biological foundations of human language are significantly different to other animals - or just an existing feature stretched to a significant extent - rather like a giraffe's neck. is a stretched version of a "normal" mammal neck. My own feeling is that virtually all our language is a product of culture - and the more rapidly a brain can absorb culture the more sophisticated the language can become  - in something akin to an autocatalysed chemical reaction.

I am therefore extremely interested in the work of Professor Con Slobodchikoff, as described in Animal Behaviorist, with video, at TheAtlantic.com. Experiment show that not only do their calls distinguish between different threats, but that the calls appear vary to convey additional information - distinguishing between, for example, between a tall human in a blue shirt and a short human in a yellow shirt. It addition there are complex "chatter" between individual prairie dogs which the researchers have no idea how to interpret, different groups of the same species living in different places have different dialects of the "same" language, while different species appear to use different vocalizations. 

Following a holiday and other distractions I am about to return to drafting my detailed notes on the evolutionary implications the ideal brain model (see From the Neuron to Human Intelligence: Developing an “Ideal Brain” Model). Research such as this, which suggests that we have underestimated the ability of animals to communicate with each other, makes it much easier to argue that our brain works in the same way as the brains of many other animals - except that ow brain has adapted to handle large quantities of cultural information. 

Looking back Half a Century on some Holes in the Ground.

The Old Farm Barns at Higher Kiln Quarry circa 1961

Between 1959 and 1962 I was studying for a Ph.D. in Chemistry at Exeter University, but spent much of my time on a different activity - exploring the limestone caves in Devon. I became Secretary of the Devon Spelaeological Society and was involved in the purchase and early work on an important geological and biological site - Higher Kiln Quarry, Buckfastleigh. However my grant ran out in September 1962 and as I needed an income I took a job in Hertfordshire. This meant that I just missed out on the creation of the William Pengelly Cave Studies Trust.

Reopening the refurbished Museum

A week ago the Trust reopened a refurbished museum and held their 50th AGM - so I decided to take a holiday revisiting Devon and get to know more about what had happened.over the last 50 years.  So I went to Buckfastleigh to attend the reopening of the museum - and to enjoy a celebration meal at the Cott Inn, Dartington, after their annual general meeting. I was also interested to see some old films showing what had happened in the last 50 years.

The Trust celebrate with a meal

However the contact with the Trust reminded me of some unfinished work from the 1960's. While I was supposed to be studying chemistry I actually made some detailed notes relating to the formation of caves, particularly in the Buckfastleigh area. However the work was never finished as I was to busy earning a living and raising a family. As a result the bulk of my observations were never published.

Filming the Time Team Programme

There was one notable exception. In about 1960 I visited a cave in Cheddar Gorge, Somerset, which was being explored by the Mendip Caving Group, and wrote up an account of the deposits there. I was contacted for more details by an archaeologist in the mid 1990's and, based mainly on my observation, a Time Team programme was broadcast in 1999. As a result I tidied up my original notes, did a literature search on later developments and published a detailed paper, Cooper's Hole and the Cheddar Master Cave, in the Pengelly journal, Studies in Speleology, Volume 17. The photo shows  Malcolm Cotter (in the green boiler suit) and me being interviewed and comes from the book Behind the Scenes at Time Team, by Tim Taylor (photos by Chris Bennett), published in 1998, which contains an extensive account of how the programme was made. Cooper's Hole can be seen in the background.

My renewed interest in the caves, and in particular a look at some of the exhibits in the museum, has reminded me about my unpublished notes - and in particular the need for lateral thinking in understanding how the caves formed. I therefore took the opportunity to make contacts to see if I can get some photographs taken of some important geological features - mainly relating to the shape of the roof of cave passages and chambers. There is no way, at 75, that I could safely re-enter the caves, and such pictures will help me to document my original notes in a scientifically usable form. The result could well be a couple of serious papers - plus further information which I could well publish on this blog.

Rural Relaxation - Getting one's feet wet

In March last year I posted this picture (A Rainfall Crisis looming in Eastern England) which was actually chosen to show how low the water had got in the College Lake Nature Reserve near Tring! College Lake is actually a disused chalk pit and when quarrying was abandoned it was estimated that that water entering the lake would seep away through the chalk. The calculations as to the equilibrium level were badly wrong and the water rose to flood the track through this gateway some years ago. However they had seem to stabilize and during the drought of 2011 and early 2012 the water actually dropped to revel about an extra foot of this gate.

Did the Neanderthals die out because humans had a better culture?

Two recent news items, coupled with earlier reports that Neanderthal children matured faster than human children, could be relevant to Neanderthal intelligence and perhaps to the species becoming extinct.

The paper New insights into differences in brain organization between Neanderthals and anatomically modern humans by Eiluned Pearce, Chris Stringer and R. I. M. Dunbar notes that while the Neanderthal brain size was similar to Homo sapiens, more of the brain was devoted to sight and controlling a larger body.

The paper Barium distributions in teeth reveal early-life dietary transitions in primates by Christine Austin et al examined a Neanderthal tooth as estimated that breast feeding in Neanderthals may have only continued for about 14 months compared with 30 months in human non-industrial societies.

More about the Denisovans DNA - and interbreeding

Following my previous post I am most interested to see the post Denisova and aDNA: an embarrassment of riches on The Rocks Remain and New Denisova and Neanderthal DNA results reported on John Hawkes Weblog. There is clear evidence that there was interbreeding between Denisovans and Neanderthals - but there is also evidence of yet another human subspecies.

Human Evolution - Asian or African Origins and Family Trees

The current New Scientist contains an article Our Asian Origins by Colin Barras which suggests that our ancestors might have moved from Africa to Asian and then moved back again and illustrates it with a modified classic human family tree – when evidence is rapidly accumulating that such a view is over-simplistic.

A typical classic human family tree
with no cousin links.
From The Quest for Human Origins

In order to understand human evolution we need to get a grip on the mechanisms – and to realise that we will never have more that a fraction of a millionth of the information we would need to get a complete picture, and the fragments of bones we find represent only some of the environments our early ancestors lived, with some environments being totally unrepresentative. Remains from different places with different features and from different dates are given different species names – but this does not automatically mean that the living creatures could not have interbred, had it been possible for them to meet.

A Simple Guide to the Relationship between Neurons, Natural Language and CODIL

I have posted the detailed discussion paper Fromthe Neuron to Human Intelligence: Part 1: The “Ideal Brain” Model and my idea is to supplement it with brief notes examining various topics, including any raised by comments. This is the first of those notes

A noun such as Macbeth, or Dagger, or Author is represented in the brain as a somewhat amorphous network of neurons which I have called a memode.

Memodes contain other lower level memodes. Thus Murderer will contain Macbeth and Crippen, while Author will contain Shelly and Shakespeare. People will contain sets such as Murderer and Author and individuals such as Churchill.

A memode may also represent a context where several nouns are associated. An example of a context would be Macbeth; Duncan; Dagger. Another might be Macbeth; Shakespeare.

The ideal brain model connect up the links – so the above two examples can be merged as Macbeth; Duncan; Dagger; Shakespeare.

As Macbeth is a Murderer we can expand the above to the context Murderer  Macbeth; Victim  Duncan; Weapon  Dagger; Author  Shakespeare. While we are only using nouns it is easy to relate this to a natural language statement such as “According to Shakespeare Macbeth used a Dagger to kill Duncan.”

CODIL was a blue sky project to try and provide a fundamentally human friendly information processor for handling a range of non-mathematical tasks. In MicroCODIL (a demonstration version that runs on the BBC Microcomputer and uses colour) the above example would be represented as

1 MURDER = Macbeth,

2   VICTIM = Duncan,

3     WEAPON = Dagger,

4       AUTHOR = Shakespeare.

While the ideal brain model works by making links within a network of neurons, and CODIL works by moving symbols around a digital store, the two processes are equivalent.

The CODIL idea was triggered by research on a very large commercial data processing system, and has been trialed in medium sized poorly structured data bases (medical and historical data), providing online tutorial material for classes in excess of 100, as a schools package for demonstrating a wide range of information processing ideas, and in the area of artificial intelligence. A package called TANTALIZE used CODIL to solve 15 consecutive Tantalizers (now called Enigma) published weekly in the New Scientist.

The parallel between the ideal brain model and CODIL suggests that the ideal brain model could probably support a reasonable level of natural language skills – but more research is required. The bottleneck as far as the basic ideal brain model is concerned relates to the speed of learning – and this issue will be addressed in Part2: Evolution and Language.

Evolution and Alfred Russel Wallace

Alfred Russel Wallace

Last night I watched part 2 of Bill Bailey's Jungle Hero - a two part BBC TV series on the life of Alfred Russel Wallace - and immediately it finished I switched to the BBC Iplayer to watch part 1. If you are interested in the origins of the ideas behind evolution, and the difficulties of persuading the establishment to change its way of thinking I am sure you will find the programmes really interesting - as I did.

From the Neuron to Human Intelligence: Developing an “Ideal Brain” Model

I have just posted a discussion paper: From the Neuron to Human Intelligence: Part 1:The “Ideal Brain” Model which will shortly be followed by Part 2: Evolution and Learning. In these papers I propose a model which suggests how the electrical activities of neurons in the brain may be related, via evolutionary probable pathways to high level activities such as language and intelligence. If the model is even reasonably accurate it could have implications in many different specialist areas where an understanding of how the brain works is relevant.

It is clear that more research is needed to establish the validity of the model and my problem is how to go about both publishing and organising any further research, especially as some of the ideas are counter-intuitive – which can make communicating them difficult. If I was a young academic just starting out on a research career and working in a supportive university there would be some relatively obvious options. However I am 75 years old, my only resource is a personal computer with access to the internet, and I currently have no active contacts with any major academic institution. As a scientist through and through I feel the idea should be followed up, and as an old age pensioner I would be happy to hand the matter over to a younger generation and enjoy retirement.

Bearing in mind my limitations the approach I have taken is to use this blog as the means of stimulating discussion of the issues and disseminating information about the research.

1. The two papers have been kept comparatively short to make them more readable. If I tried to address every possible research issue that might be relevant it would take me far too long and the texts would become unreadable.
2. Anyone who want to see more examples of how CODIL works, its applications, etc. can look at the many CODIL papers already online. In addition I have other reports (some only in draft form) and actual computer listing of other applications – and these can be posted online if appropriate.
3. If anyone has difficulty in understanding any points, and/or has specific questions – I will be happy to answer them via this blog. In particular if you are doing some brain related research (in the widest sense) send me details (remembering I may have problems with pay walls) and I will happily give you my suggestions. After all a good test of my ideas is whether I can answer your questions convincingly.
4. If there is enough interest I will try and make arrangements to make MicroCODIL software and manuals available to anyone who has access to a BBC Microcomputer. (Because the computer has become something of a cult survival second hand ones are often available.)
5. Should I be able to help an existing university research project by giving a talk, attending a seminar, etc., I am happy to do so. Even if you don't agree with me exposure to controversial ideas can help everyone to start thinking outside the box.
6. If a particular research group wanted to resurrect any of the CODIL programs and applications, or use them as a basis for an “ideal brain” simulation I would be happy to advise.

Rigid Legal Rules can Kill

Some of you may not realise the symbolism behind this site's logo. Occasionally a news item can bring back sad memories.  This evening Channel 4 News reported that:

Two mothers, both of whose 17-year-old sons committed suicide after being detained by the police, wept in court today as a judge ruled the treatment of 17-year-olds in police custody is unlawful.

Coelacanths and Confirmation Bias

I recently read an interesting blog by P Z Myers entitled Coelacanths are unexceptional products of evolution - which discussed why it was inappropriate to call them "living fossils" which were "slowly evolving". It included the following example showing confirmation bias in the scientist researching this interesting fish:

So why is this claim persisting in the literature? The authors of the BioEssays article made an interesting, and troubling analysis: it depends on the authors’ theoretical priors. They examined 12 relevant papers on coelacanth genes published since 2010, and discovered a correlation: if the paper uncritically assumed the “living fossil” hypothesis (which I’ve told you is bunk), the results in 4 out of 5 cases concluded that the genome was “slowly evolving”; in 7 out of 7 cases in which the work was critical of the “living fossil” hypothesis or did not even acknowledge it, they found that coelacanth genes were evolving at a perfectly ordinary rate. 

Research does not occur in a theoretical vacuum. Still, it’s disturbing that somehow authors with an ill-formed hypothetical framework were able to do their research without noting data that contradicted their ideas. 

Rural Relaxation: I see a "Dragon" on Bookham Common?

I like to spend and hour or so each day walking, preferably in the countryside. Yesterday I was walking past the ponds on Great Bookham Common, Surrey, and was surprised to see in the distance a large animal which had come down to drink on the far side of Lower Hollows:

Unconventional Ideas and the establishment

There have been further comments on Robin Ince's post "The Fascism of Knowing Stuff" including some relating to the idea  of interesting unconventional ideas being suppressed as a result of peer reviews and Nullifidian wondered whether there were any real "anonymous" scientists who had problems - so with the following comment I stood up to be counted:

Don't confuse Science and Technology.

Having read Robin Ince's post "The Fascism of Knowing Stuff" I felt he was confusing Science and Technology and added the following comment to his post.

I agree with your definition of science but at the end you are talking about technology as if science and technology were one and the same thing. Of course the two are closely linked but what the average person sees is not “pure” science but rather technology – and they only see that technology because someone is making money out of it!

There are many problems. If an early version of a technology is commercially acceptable better versions can be blocked because people have adjusted to the original technology (which may have become an international standard) and there are more people wanting the old technology (even if science has shown it to be inferior) than would benefit in the short term if the improved technology were introduced.

A good example is the QWERTY keyboard which was used on early typewriters, then on teleprinters, which were used as early input devices for computers … Much excellent research has been done on better keyboard, using the latest scientific advances – but QWERTY is still with us, although its is being replaced in some areas by completely different forms of information input.

The problem of competing technologies is illustrated by the triumph of VHS over BetaMax (which was said to be technically better) because the real battle was who would get the biggest market share – as people would buy the system with the biggest collection of recordings.

This raises a potential trap – if a new technology comes along and is extremely successful because there was no competition its total domination of the market would make it almost impossible to develop and market improved versions – and as a result it could be difficult to fund blue sky scientific research which questions the foundations of the technology.

Let me suggest where this may have already happened. The stored program computer emerged in the 1940s and was soon seen was a money spinner – with many companies rushing to get a foothold in the market. The rat race to capitalise on the invention has resulted in systems which dominate everyday life in much of the world, where the technology is taught in schools and everyone knows something about how computers work – if only in the form of an inferiority complex because “they are too difficult for me”.

In fact it is considered as an unavoidable truth that computers are black boxes where the internal workings are incomprehensible to the computer user. But the stored program computer is incomprehensible because computers were originally designed to process mathematical algorithms carrying out tasks which the average person would also find incomprehensible. The problems computers were designed to solve are about as far from the problems faced by early hunter-gathers as it is possible to imagine.

There must be an alternative. It is well know that nature has produced information processing systems (called brains) which start by knowing nothing (at birth) and can boot-strap themselves up to tackle a wide range of messy real world tasks. In the case of humans their brains can exchange information and people can work together symbiotically.

So which scientists in the 1940s was saying that blue sky research into whether a “human friendly computer” that worked like a brain would be possible?. … or in the 1950s? … or in the 1960s? … …

If you look through the literature virtually everyone who ever though about the problem was taking the stored program computer for granted. You will search the old literature in vain – and when people started to worry about the human user interface it was about writing programs to hide the inner black box from the human user. No-one was going right back to first principles to see if there was an avoidable weakness in the use of the stored program computer. And – because they were thinking of analogies with the stored program computer – it was taken for granted that the brains “computer” must be so clever it was very difficult to understand because it was “obviously” difficult to program. In effect the very successful technology was beginning to influence the way that scientists were thinking about research into how the brain works.

In fact in 1968, backed by the team which built the Leo Computer (the world’s first commercial computer), work started on early studies with the purpose of designing a fundamentally human friendly “white box” information processor. I was the project leader and the project ended up under the name CODIL. The problem we faced (which has got worse over the years) is that even if it had been successful (and results with software prototypes were very promising) it would have to battle with the established stored program computer market. Look at the investment in hardware, applications, data bases, trained staff, public understanding, etc. etc. of conventional systems and the inertia against possible change is probably valued in trillions of dollars.

To conclude I suggest that, because the computer revolution was technology led, key blue sky research was never done – and anyone proposing such blue sky research now is more likely to be greeted with hostility rather than adequate research funding.

~~~~~

Nullifidian replied - and the relevevant part of his reply was: 

Finally, I didn’t use the phrase “anonymous scientists” to invite people who thought that peer review had done them wrong to submit their tales of woe. Frankly, I don’t care. The point I was making there was to say that there are plenty of ways to get information out to the scientific world, and publication is actually the least efficient of these and arguably mostly irrelevant. Conferences, preprints, presentations before other university departments, etc. are where the scientific action is. However, all these means of getting around the peer review process require that your work actually be as interesting to your colleagues as you think it is.

In your own case, you haven’t demonstrated that the peer review system has suppressed a scientifically worthy idea. You cite the absence of people “go[ing] in [your] direction” as evidence that these views have been “crushed by the establishment at an early stage”, but an equally potent hypothesis is that your ideas are unworkable and nobody wants to spend their time trying to make the unworkable work. While I can’t say without seeing your ideas in full, the notion that you can just switch from computation to talking about the brain without any apparent background in neuroscience is another indication that you’re a crank. So is the use of coined terms and irrelevant jargon. In what way is a brain similar to an “ideal gas”? An ideal gas is hypothetical state in which the molecules all randomly moving small, hard spheres that have perfectly elastic and frictionless collisions with no attractive or repulsive forces between them and where the intermolecular spaces are much larger than the molecules themselves. None of these things are true in practice, of course, but they’re close enough to the model in most cases that it makes no difference. Now, neurons are not small hard balls, they don’t move in random directions and collide elastically, the synapses are not vastly larger than the neurons, and there’s no way the concept of an ideal gas appears to work even as a metaphor. So I’m not convinced that the rejection of your ideas by an unfriendly peer review system is evidence that the “establishment” is wrong.

I have now replied:

First let me thank you for your critical comments – as the enemy of good science is confirmation bias – and what is needed to explore controvercial ideas is open no-holds barred debate on the issues. I have now posted a discussion draft “From the Neuron to Human Intelligence: Part 1: The ‘Ideal Brain’ Model” (http://trapped-by-the-box.blogspot.co.uk/p/blog-page.html) and have added a section on nomenclature specifically because you raised the subject. 

Now responding to your specific comments let me start by reminding you that I said “despite enormous efforts in many different specialist field, there is no theory which provides a viable evolutionary pathway between the activity of individual neurons and human intelligence.”

If you think this statement is wrong I would be very grateful for a reference to a paper which describes such a model. If you can’t provide evidence of such research why are you so hostile to the suggestion that someone thinks that they might have a possible answer?

For instance you introduce a straw man argument relating to the analogy between my “ideal brain” model and an “ideal gas.” Of course I would be a crank if I thought neurons were little balls bouncing around in the brain – as you are suggesting. The whole point of the “ideal gas” model is to strip everything down to the bare essentials. You start with an infinite brain filled with identical neurons (cf. An infinite container filled with identical molecules). Interactions between neurons are not by collisions but by electrical connections which carry signals of variable strength. (In theory every neuron is connected to every other one – but in the vast majority of cases the strength of the interaction is zero.) In an ideal gas the three properties of interest at pressure, volume and temperature, while in the ideal brain we are interested at the ability to store patterns, recognise them, and use them to make decisions. Another similarity is that both models work pretty well in some cases – for instance the ideal brain model suggests one reason why humans are prone to confirmation bias – and when the models start to fail the models can be used to explain the differences.

Your comment about switching between computation and talking abut the brain is interesting for two reasons.

Any research model which attempts to link the neurons to human intelligence will involve many different disciplines in fields such as psychology, childhood learning, animal behaviour, linguistics, artificial intelligence, and neuroscience, and in addition will undoubtedly involve modelling on a computer. I would argue that what is needed is the ability to stand back and be able to see the wood from the trees – and that have too much mental commitment in any one speciality could be a liability. You seem to be suggesting that neuroscientists are some kind of super-scientists who have a monopoly on holistic approaches to how the brain works.

However the comment is interesting because it pin-points the problem I have had. My ideas became trapped between a rock and a hard place. I worked as an information scientist (in the librarian sense) before entering the computer field and was used to seeing how people handled complex information processing tasks. I then moved to computers and concluded that there were serious flaws in the design of stored program computers – suggesting a fundamentally different model that reflected how people handled information. I could not get adequate support from the computer establishment because computers were so successful that there couldn’t be any serious flaw in their design, and even if there were problems there was so much money to be made ploughing on regardless that any time spent on blue-sky-research into work that questioned the ideas of people like Turing was a waste of time.

At the same time I was getting comments from other fields that I could not be modelling how people think because the standard computer model was wrong and as I was a computer scientist I must also be wrong! I am sure your critical comment was based on a stereotyped view that tars all computer scientists with the same brush.

TANTALIZE - the School Colours problem - and peer reviews

TANTALIZER No 226     NEW SCIENTIST 

 SCHOOL COLOURS 

    "Tell Me, Professor Pinhole, which school does your daughter Alice go to?"
    "Let me think. Is it the one with the orange hat and the turquoise scarf? or with the khaki blazer and orange emblem? or with the pink blazer and orange scarf? or with the khaki scarf and pink emblem? or with the khaki hat and turquoise emblem? I fear I cannot recollect."
   "Good Heavens, Professor! However many schools are there?"
   "Just four and I have one daughter at each. Bess goes to St Gertrude's, Clare wears a turquoise hat and Debbie wears a khaki emblem. St Etheldreda's flaunts a pink scarf, St Faith's an orange blazer and St Ida's a pink hat."
   "And whose are those clothes flung down on the floor over there?"    "The turquoise hat and the khaki blazer belong to different girls. As for the turquoise blazer, well, I think you might work out whose that is for yourself." 

Martin Hollis

(For solution see the paper on TANTALIZE)

In fact some of the work I did with the TANTALIZE package in the 1970s is relevant to the brain modelling work I am doing now - and a little of the history is relevant. 

In 1972 I started the work of implementing the second version of the CODIL interpreter on the 1903A computer at Brunel University with a view to concentrating of open-ended commercial and data base tasks once I had got the system up and running.  One day I had a discussion with a colleague, Roland Sleep, and he pointed out that while there was a lot of hype about Artificial Intelligence what was actually being done was comparatively simple - and he lent me a copy of a Ph.D. thesis on one of the leading problem solver packages. Within three days I had CODIL up and running the key examples in the thesis. I followed this up and used CODIL to implement a problem solving package which I called TANTALIZE - which, among other things solved the Tantalizer "brain teaser" puzzles for 15 consecutive weeks as they were published in the New Scientist. The first paper I wrote was TANTALIZE with included a number of examples of CODIL on its own and using the problem solver.

The reason for mention TANTALIZE now is that CODIL was not designed to be a programming language, but as it is designed to reflect the user's view of his information processing task it has to accommodate users who want to use it to "write programs". TANTALIZE is by far the biggest CODIL "programming" task written and can be considered as a sophisticated production rule system, written in, processing, and obeying production rules. The first phase is to ask the user a series of questions about the task, and also any general information on the type of task and the resources needed. The second phase turns the user input into a set of production rules and in some cases the package uses dynamic learning to sort the rules into a "most likely to succeed" order - which can lead to orders of magnitude reductions in the time needed in the third stage. The third stage take the optimised production rules and uses them to search the problem space and present the answer.

I continued solving problems with TANTALIZE but immediately ran into difficulty with the peer review system in getting A.I. papers accepted - so I simply switched to other application areas and dropped the work on heuristic problem solving. After all CODIL was not designed to handle small well-defined closed problems - but naturally t can do them because they are a subset of the bigger less well-define open-ended real world problems with which it is really concerned.

In retrospect it is interesting to look at why, for example, a paper was rejected as "Too theoretical - will never work" when I had reported in detail the way the package actually solved a wide range of problems. Or why I was told about another  that if I wanted to get papers accepted I should use the POP-2 programming language. A paper sent to a leading journal in the USA came back with two vitriolic reviews, one reviewer admitted to not understanding it, and there was one favourable review. I was so cheesed off by multiple rejections at this stage I just junked it and only some years later rediscovered the covering letter from the editor (who would have know who the reviews were) which ended with the advice that I should continue as he felt there must be something in it to have annoyed two of the reviewers so much.

Of course the real problem is that we are all trapped in the mental boxes we have constructed for ourselves during our lifetime and my mental box did not overlap with the mental boxes of the majority of the  A.I. establishment. For instance I approached the problem from the angle that there are many very complex open-ended problems - with no simple solutions - and to me the logic puzzles were a trivial artificial subset of the real world - where there were precise pre-defined rules and unique answers. The A.I. establishment at the time concentrated on applying formal mathematical models to closed tasks - such as game playing - in the belief that this was the way forward to modelling intelligence. My papers did not fit in as they were not expecting a solution coming from the area of open-ended and poorly defined tasks. Looking back it is clear that I was not really aware of how counter-intuitive some of my ideas were. I suspect that most genuine "outside the box" research has similar problems with peer review systems for both academic publication and research grants.

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If you read the TANTALIZE paper earlier you will find the missing sections have now been added.

An account of the TANTALIZE package published in the New Scientist is below the break.

Speech-like vocalizations in our primate cousin - the Gelada

One of the difference between humans and other primates is our wide range of vocalizations - which we exploit in our use of  an extensive spoken language. Recent work by Thore J. Bergman of the University of Michigan on the Gelada (a baboon species) show that it makes a "wobble" like noise that has some has some similarity to human speech.  There are also some other references to lip-smacking as a means of signalling in primates.

Speech-like vocalized lipsmacking in geladas, Thore J. Bergman, Current Biology Vol 23 No 7

All Neurons are potentially "Mirror Neurons"

Commenting on the post "Mirror Neurons" and reflect Hatred" by Daisy Yuhas I wrote:

I am currently working on an “ideal brain” model (think of physical science's “ideal gas” model with neurons instead of molecules and links between neurons like collisions between molecules). One of the features of this model is that any network of neurons can work in two ways – recognising or doing – and the two roles are dynamically interchangeable. This suggests that there is not a class of “mirror neurons” because in theory all neurons can work in this dual manner.

Of course some activities are more relevant to recognition and some more relevant to doing, and there are some where it would be difficult to carry out the kinds of experiments that lead to scientist postulating that there was a special class of neuron.

What is interesting is that a basic feature of how neurons work could also be important in understanding how animals and people interact. The relevance of the mechanisms to empathy and social interactions is extremely interesting – but one must be careful to recognise that how much attention the mind gives to something will be affected by how relevant the activity is seen to be – and the observed levels of “mirror neuron” activities in experiments may be more related to motivation to pay attention than any specific factor in the part of the neural network being monitored.

What I find more interesting is that the ability of the mind to mirror what other people/animals are doing could be very relevant to some kinds of learning. Our brain sets up a neural pattern which recognises the activity and the tries to execute it to repeat the actions.

Looking for the "Neural Code"

Going through an older section of my email inbox I found a Scientific American link to John Hogan's blog post Do Big New Brain Projects make sense when we don't even know the “Neural Code” in which he wrote:

Neuroscientists have faith that the brain operates according to a “neural code,” rules or algorithms that transform physiological neural processes into perceptions, memories, emotions, decisions and other components of cognition. So far, however, the neural code remains elusive, to put it mildly

The neural code is often likened to the machine code that underpins the operating system of a digital computer. According to this analogy, neurons serve as switches, or transistors, absorbing and emitting electrochemical pulses, called action potentials or “spikes,” which resemble the basic units of information in digital computers.

I prepared the following, perhaps too lengthy, comment but when I came to post it I got a message that the page had been moved - and all attempts to find it resulted in irrelevant pages on the Scientific American web site. So I am posting my response below:

------------------------------

I would argue that the problem with modern brain research is an inability to see the wood for the trees. Of course if you look in detail at the brain things get very complex. But such complexity is common in science. The key idea underlying evolution is very simple – but when you look at individual cases in detail there can be enormous complexity. The same applied in medieval times when the movements of the known heavenly bodies appeared to be very complex – until it was realised that things became much simpler if you calculated the motions of the planets using the sun, rather than the earth, as a key reference point.

The problem with the human brain is that, as John Hogan says, we don't know the “Neural Code” and virtually everyone is looking at the problem in ever greater detail – apparently on the assumption that the harder you look at the fine detail the more certain you are to find out the shape of the wood!

I have been trying to stand back and get an overview and have come up with an “ideal brain” model which in some ways parallels the “ideal gas” model in physics. All neurons (like all gas molecules) are identical – and the dynamic links between neurons are like the dynamic collisions between gas particles. Using such a simple model it is possible to “grow” a brain that can remember and use more and more complex concepts – with the complexity of the most advanced concepts it can handle depending of the brain's capacity and time for learning. The model explains consciousness and can predict detailed observations about the brain - for instance the so-called “mirror cells” in the brain turn out to be nothing special as the observations simply reflect the way that all neurons work in the “ideal brain”. In addition it is possible to ask how human “intelligence” might evolve and this approach predicts a major tipping point (rather than some major genetic “improvement”) which produces and “explosion” of “new ideas” when “cultural intelligence” becomes a more effective tool than the innate biological “intelligence” of the “ideal brain” model.

The problem with the model, and possibly the reason why it appears not to have been explored before is that to a “culturally matured” mind (and all people accessing this text on the internet will be culturally mature) the model involves several counter intuitive steps.

1. The model assumes that at the genetic level the only significant difference in the processing mechanisms between our brains and most animals relates to supercharging effects (more capacity, more links, more effective blood supply, etc.), and that if there is a difference the model actually suggests a reason why we might be genetically less intelligent that some other animals! Before you shout me down over this “outrageous claim” I should point out that the model suggests why culturally supported intelligence is infinitely more effective than the genetic intelligence foundation on its own.
2. You have to forget everything you have learnt about computers and algorithms. The definition of a stored program computer model requires there to be a pre-defined model of the task to be performed. The “ideal brain” model starts by knowing nothing about anything and has no idea what kinds of tasks it will be required to carry out. Virtually all it does is store and compare patterns without having any idea what those patterns represent. Once you start looking in great detail at how specific name tasks are processed you have taken your eye off the ball - as you are asking about what the brain can learn to do - and not what the underlying task independent mechanism is.
3. Everyone knows there can't be a simple model of a Neural Code – because with so many people are looking someone would have found it if it existed - so there is no point in looking ...
4. My research has “reject” stamped in all the standard “Winner of the Science Rat Race” boxes. I make no secret that I am 75, am not currently associated with any established research group, and the only facilities I have are a P.C. in a back bedroom, access to the internet, and access to some old research notes on a long abandoned blue sky project which was trying to design a human friendly white box computer to replace the standard human hostile black box computer everyone takes for granted.

If you are interested I hope to have a detailed description of the “ideal brain” model on my blog later this month.