Why our brains are shrinking

A letter in the current New Scientist (April 22) asks the question "But why have we never filled up a brain?" and wonders why we appear to have evolved brains with spare capacity. I have just posted the following reply:

There are good evolutionary reasons why our brains are bigger than we need to survive. About 100,000 years ago the rate at which we invented new tools started to increase and as our tool-making knowledge increased we might have expected a significant increase in brain size. The opposite is happening and our brains are now slightly smaller than our immediate ancestors.

An ongoing re-assessment of an unconventional research language called CODIL suggests the reason. The original research looked at how humans and computers might work together on complex tasks and the current analysis suggests a direct relationship between the brain’s neural nets, language and intelligence. Animal (and early human) brain size is clearly limited by the time spent on trial-and-error learning in a single lifetime. However, once a self-modifying language has been invented (no need for a change in brain size or structure) a major tipping point is reached. One generation can now pass information to the next generation as abstract concepts, reducing trial-and-error learning times.  High level generalizations learnt in this way also need less neuron storage space and the savings increase as language became more sophisticated over the millennia.  In addition modern civilization allows us to call on shared knowledge (other people, books, etc), and so we no longer need brains as big as those of our pre-language ancestors in order to survive, and are actually left with some spare capacity to enjoy science, the arts, and the world around us.

Of course one is limited in what one can say in a short letter (New Scientist letters are rarely more than 250 words long) and it is worth saying a few words here to expand the above point.

 

Bottom up learning by neural net is an expensive business and brains cost a lot to run, and have to compete for resources with the other organs of the body in the evolutionary struggle to survive. But normally everything an animal learns in its lifetime is lost when it dies, so there is a practical limit on how much time an animal spends learning useful patterns rather than eating, breeding and evading predators. Different species have adopted different strategies and one, adopted by the primates is to concentrate on a small number of infants, and protective parents, to maximise learning time.

 

Early humans found simple tools helps survival and needed to pass on their tool-making skills to their children. So a slightly longer learning time helped and over some five million years our ancestors brains very slowly grew bigger allowing them to make somewhat more sophisticated tools. However the more steps it takes to make a tool the harder it becomes to learn how to make it by trial and error copying.

 

This is where CODIL comes in. CODIL was designed to allow humans to "teach" computers in a way that can be related to neural nets - and can be considered as a model for early human language as a means of making tools, with language itself being a taught tool, which can morph from processing patterns (normal animal learning) into set processing (handling abstract ideas and generalizations) and on to rule building (instructions for making tools).

 

What appear to have happened is that until about one and two hundred thousand years ago humans brains could be considered as little more that animal pattern recognising systems which learnt by trial and error - including trial and error copying of their parents using a simple language. Then language developed to a point where it allowed more complex tools. This meant that having language allowed each generation to make more powerful tools, including a more powerful language, which in turn enabled even more powerful tools to develop. A tipping point was reached which as equivalent to an auto-catalysed chemical reaction which, once it has started, proceeds at an every increasing rate.

 

The process also means making more and more powerful generalizations, which can be stored more efficiently in the neural network of the brain. (For instance learning about "mammals" and their key differences uses less memory than learning about a large number of species bottom up.) As long as our ancestors still lived in family-sized hunter gatherer groups most of the brain would still have been needed for survival. However once people started living in larger communities specialization would have started - and survival would depend on the skill mix of the community rather than individual skills. In fact the more we depend on others, including knowledge in books, etc., the less general survival skills we each need - and this allows our brains  - which evolved to learn as quickly as possible - to spend time on creative mental activities which have no obvious survival value!

 

 

 

 

How Humans invented Natural Language and why Animals don’t have it

Each species will balanced the use of resources between activities such as feeding, breeding, avoiding predators, and learning how to optimise these resources by using the brain. There are many different evolutionary strategies. For instance some fish lay millions of eggs while humans have small numbers of young and use their brain to maximise the survival of each youngster. However we can be certain that no animal evolves an organ bigger than it needs, and if conditions change an organ will shrink if it is bigger than necessary. This will apply to every organ and function and no species will not evolve a brain bigger than it needs.

Humans are good at uncritically accepting what they are told!

I have just been reading a discussion post by Maxi-Pad entitled

Science channels, If they show it, It must be true

in which he says:

I have been having a very interesting (by interesting meaning mostly ridiculous) conversation with a coworker who believes in the bible literally and that the universe is 6,000 years old. We can get pass the "don't even talk to people like her, it is pointless." However, as we kept conversating, she proceeded to tell me that she does believe in ghosts. When I asked her why she simply replied "well, don't you? They have all these shows in National Geographic and all the science channels, If they say it, it has to be true. Thats what the channels are about." I was shocked. I proceeded to tell her that just because something is shown on TV, it doesn't mean its true. She was very surprised to hear that and proceeded to ask me how is that possible. That is the moment I realized that there actually are people out there that believe everything they see on TV just because the channels claim to be science channels.
This is obviously impacting the science community with almost no effort it feels. I would like to know any thoughts or ideas about this, or even what can we do to make people like this woman who was never properly taught from a young age to just learn to question.

I replied:

Lets be honest. Most things that most people "know" is taken on trust from what other people (including books, TV, etc) tell them.

If we think about the evolution of the brain the critical factor relating to the size of the brain is learning time. There is a limit to what you can learn by simple trial and error copying. Once a species can support a culture which provides better survival prospects the faster it can learn from other members of the species the better. So once language started it provided a very quick route to absorb cultural information. Evolution meant that the human brain responded by providing an express learning route - if someone tells you something learn it without question - because (on average) it is far better than anything you can learn by personal trial and error experiments.

My work on the brain's neural code suggests that the brain's basic mechanism is automatically slanted towards what psychologists call confirmation bias. Put the two things together and the human brain is prone to "follow my leader" and be strongly influenced by the first things it learns - which acts as a filter to only accept things which it has know are "true" for a long time. And because a child's brain is optimized to suck in new information at speed there is no checking real checking that information from different sources is logically consistent.

Our brain is not, at the biological level, optimized to understanding sophisticated mathematical logic. If a child is repeatedly told, at a receptive age, that anything which disagrees with the bible is illogical, and that everything that supports the idea that the world was created a few thousand years is true we should not assume that the resulting adult is stupid - we should blame the education system that primed their brain with such ideas.

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.

--------

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.