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.

We need to realise that every step in the human family tree will involve couples who are cousins (or closer) - although in some cases the cousin relationship could span many generations. Some years ago I did a calculation to show that every English person is virtually certain to be descended from everyone who was living in England in 1066 – so all the English are cousins if you go back about 40 generations. The current races of mankind go back perhaps 2000 generations and despite differences are definitely members of the same species. Our Neanderthal and Denisovan cousins represent perhaps 20-40 thousand generations. Our great ape common ancestors are perhaps 500 thousand generations back. We know that our ancestors interbred with the Neanderthals and Denisovans – which suggests that groups that became separated can interbreed successfully when separated by many 10s of thousands of generations.

But evolution can changes species quite faster than that in selected directions – for instance adult lactose tolerance in races which drink animal milk evolved in less than 200 generations. Other features such as the number of vertebra in the neck have remained the same over millions of generations. So what would happen if two groups separated for a long period meet and interbred. For instance during a 500,000 year separation a tool-making group could have evolved more dexterous hands, while the pursuit hunting group might be able to run upright faster. Some of their children would have the benefit of both more useful hands and greater mobility and hence have a big advantages in the survival stakes. The differences might be such that scientists later finding bone fragments assign a new species name, especially if the new remains were incomplete and not in the home territories of either of the parental groups.

I have earlier looked at the evolutionary pressures on the African Plains and it is likely that our early ancestors split into groups by developing cultures resulting to different types of environments and food sources. (A similar effect can be seen with animals with a complex social structure such as orca which uses very different hunting techniques in different parts of the world adapted to different prey.) Evolution will tend to select not only at the individual animal level, but also at the group level, favouring groups which are best at exploiting their environment, and changing when the environment changes. Some groups may have been, on occasions, more mobile, and some undoubtedly moved out of Africa into Asia and Europe, and many generations later moved back, and interbred with their distant cousins.

While it is clear that many groups will have evolved in such a way that they definitely represented different non-interbreeding species, there may soon be sufficient evidence to allow future evolutionary human family trees to show splits and reunions – with the different branches contributing in different ways to Home sapiens.

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.

Different laws treat the age at which children become adults differently and in the above case the police had assumed that while 16 year olds were children they could arrest 17 year olds without telling their parents. Following these two deaths, and several more to lethal cases the courts have now ruled that the legal age for the purposes of police arrest was 18.

Basically the law assumes that 17 year olds are vulnerable - and their parent need to be involved - but a miracle occurs on their 18th birthday and they are no longer vulnerable and don't need the help and support of their parents.

Lucy

Of course this clarification of the law would not have help us in 1984 as Lucy was 20 when she was arrested. The point is that she was very vulnerable - and too ill to really be able to look after herself.  She had been already been remanded to Holloway, before we accidentally discovered what had happened. Only a month or so before she had been an in-patient in a psychiatric hospital (for part of the time on section) and she had become manic and developed a hated of taxi drivers (possibly after having been raped by one). It appears that the hospital decided it didn't want her back and as she was legally an adult she was not "vulnerable" her family was not told.

The period she spent in the "Muppet House" in Holloway not only destroyed her mentally, but seriously affected the rest of the family who could do nothing but see her going to pieces. After a Court of Appeal ruled that a fellow Muppet House prisoner should have received medical treatment and never been sent to prison a hospital bed was found for Lucy - but too late - as she killed herself on an anniversary related to her arrest.

Belinda

But this wasn't the end as the whole family was seriously affected - and the post traumatic stress disorder caused by what happened was one of the factors leading to the closure of the CODIL project. Her sister Belinda had a breakdown and after being "wrongfully arrested" in the same police station - was admitted to a psychiatric hospital. Due to a clerical error the police gave her a letter which indicated she had been charged, and a few days later she killed herself. 

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.