Science with Commonsense

When it seems like a science result is about providing proof for commonsense; this would be bad if it was that we needed proof of commonsense in order to regard or appreciate it. But then, it is good to get some scientifically originated affirmation of sense that should be common: to see the science of the sense.

When the results of a scientific investigation contradicts true commonsense, they may tell us that it is counterintuitive, trying to overlay our doubts with grammar—semantics. But commonsense ought not be conflated with intuition, even if their outcomes might be similar.

So, if a ‘scientific’ result contradicts true commonsense, then, the investigation probably got something wrong, somehow. But then we know that the scientific method, which is a very commonsensical process, is very much involved in the identification of sense that we can make common.

A love-hate-love relationship (View 2)

At first it was easy,

then it became a little challenging.

Things had slowed down.

A little later with a spurt of will,

he picked up pace and continued,

seeking to be diligent;

the same and different.

It felt harder.

First she loved him;

then she hated him.

And then again she loved him

and hated him after that;

even more so a little later.

Then she loved him again;

coming to hate him eventually some time later.

And finally, she loved him,

accepting her choice.

Demanding persistence, he gave it that;

until he hit a brick wall

and kind of got quenched.

He came back again to climb over

in a breakthrough;

going on for a while

and slowly getting weary.

Finally he let go to say,

“I love you anyways.”

What’s the algorithm behind the behaviour

Algorithms are data processors
If the data is bad: garbage out
It won’t matter what the process is
If the algorithm is bad: garbage out
It won’t matter what the input is

Behaviour is the result of an algorithm
It takes in data and information
And produces garbage or wisdom

What’s the algorithm behind the behaviour

A love-hate-love relationship

First you loved her, then you hated her. And then again you loved her, and hated her after that, even more so a little later. Then you loved her again, coming to hate her eventually some time later. And finally, you loved her, accepting your choice.

At first it was easy, then it became a little challenging, slowing you down. A little later with a spurt of will, you picked up pace and continued, seeking to be diligent, but it got harder. It demanded persistence and you gave it that, until you hit a brick wall and got quenched. You came back again to climb over the wall in a breakthrough, going on for a while and slowly getting weary. Finally you let go and say, “I love you anyways.”

Your Brain Learned to Control Your Organs

Your brain learned to control your organs. Very little came hard-coded at the start.

Our brains developed first with basic firmware, and not the full operating system, with models of organ behaviour et al, learned. By virtue of its awesomeness, it built its own operating system. If it learned wrongly, disease and abnormal situations occurred. Examples might be epilepsy and some congenital disorders.

But the brain would not be able to cause normal operation if the organs didn’t function the way the did. Every organ has a voice, and a language the brain learns, much in the same way that children pick up the language of their environment as they grow up. This learning starts in the womb. Upon reading what an organ says, if it responds wrongly, it gets a message it interprets as negative. And it tries again with something new, but now more informed. The process iterates at incredible speeds so that the organs do not roll into disequilibrium or instability.

Every organ is an independent machine with inputs, outputs, and a messaging Protocol. The brain figures out which is positive and negative after it attempts control until it figures out what really works. kind of like using what they call ‘system identification’ in the field of control engineering. We’re basically saying that the model for/and control is learned along the way. So that we move from apparent randomness to perceived order. What we call ‘Genetic algorithms’ also might mimic this learning process: starting from an initial guess and iterating through better and better ‘guesses’ (of models and associated control) based on the brains perception of the responses, until ‘flow’ is achieved.

The brain learns to control the heart and heart rate, and to do so optimally, the same way it learns a habit. It seems that once it identifies a feasible direction, equilibrium point, or a way to make an organ work, it seeks to reinforce the associated neural firing pattern by driving the same pattern when close/similar stimuli to the originating stimuli for that pattern exists. This is like the OGY control method for chaotic systems in a way, and how it is possible to sail around the world:

Where am I?
Which wind can/should I catch?
Catch it and ride
I hope it leads home
Jump from wind to wind
I hope it leads home
Jump from wind to wind
Getting closer in general
Repeat until objective achieved.

Its interpretation of overall stability, efficient global equilibrium, or effective local behaviour might be the basis of the brains response to organ behaviour. It is a learning machine that learns equilibrium oriented experiences; perhaps identified by dopamine, endorphins, and similar chemicals. Practically, it attempts to estimate the stable state of the functioning of the organs by seeking what it interprets to be peace in the long run and minimising what it sees as pain.

We could expand this to also say that the brain uses the same mechanism to arrive at ‘all’ its decisions and norms: a kind of reinforcement learning seeking peace, long term. (We may add our personal psychology too.) Reinforcing what leads to peace and what combats pain, as perceived. And then, once it concludes that it has learned adequately or correctly, it feels the need to remain the same (as with the phantom limb phenomenon).

Reinforcement learning makes the brain an obsession generation engine; both good and bad, useful and wasteful. And especially as it got positive feedback then the process started, it will release ‘feelings’ that tempt the person to act such that it can reinforce and establish the associated patterns of peace.

Read, record, respond: the brain does that. Sometimes, ‘respond’ comes before ‘record,’ with reflexes and all the learning going on. Think of a baby crawling. The brain learns its limits. And as the limits expand (stronger bones and muscles), it launches him to his feet over time. Apparently, it must test limits too.

Epilepsy? The brain has a reward circuit for it, I think. It probably ‘feels’ good (or right) when it happens. But why, or how?

If a situation persists, then it may be that we enjoy it in some way. Perhaps the brain thinks that it (epilepsy) is the better of two evils: in the sense of the body temperature rising because of malaria. The brain seeks to reinforce behavior or firing patterns it is more comfortable with (still the peace motive). So if it were not okay with the stimulus
(or neuronal response) that leads to epilepsy? It will try not to reinforce it. Can’t this habit of the brain be replaced?

An example of neuroplasticity: the more a ‘naturally’ grumpy person thinks good happy thoughts, the happier they feel, and therefore, the happier they get. So that they evolve to become, at the least, not naturally grumpy. Neuroplasticity is thus, perhaps, the basis of NLP (Neuro-Linguistic Programming), hypnosis, and other such methods for behaviour modification. Also why/how meditation on the Bible changes you. The assumption is that, ‘behaviour,’ whether internal (as in the ‘natural’ function of the body system), or external (as with out attitudes) are represented by patterns of neuronal connections, firings, and firing patterns.

So a question arises: couldn’t we ‘cure’ diseases like epilepsy by harnessing the neuroplasticity principle? By facilitating and/or learning new methods of control? Perhaps this has been explored already.

Neuroplasticity is usually spoken of, it seems, in the context of learning and healing; but there is more. We could say that neuroplasticity is the fundamental attribute of the brain, because that most of its functions and functioning result from it. It is a primary characteristic that makes the brain, the brain. And it results from the design of the brain as a learning machine that evolves itself, significantly motivated by its connections and perceptions.

PS: armchair neuroscience

The Purpose of Law

The need for law is the need to make a man a certain kind of man; to make a society a certain kind; to make a system a certain kind. It says that the man, or the system, has a potential/tendency to decay, deteriorate, or dissipate energy wastedly; to attain to an undesirable state, one that flows from the opposite of truth or the accepted facts.

What are we really doing when we make laws for our children and ourselves?
Every law that the government produces makes a statement, not just of what is legal, but also of what we should aspire to: how it wants us to be. Law is a codification of the ethics (imposed rules) of our existence, citizenship, and residence.

The purpose of law is to make the perfect man; the purpose of law is to make the ideal citizen; the purpose of law is to make the ideal society. But the ideal does not exist where the law exists, otherwise there would be no need for law.

If full love ruled, then perfection would be real. Husbands, fully love your wives, and wives, your husbands. (Love for your children is implicit in loving your spouse.) Imperfect people leading ideal lives.

So the law functions to train one’s conscience. Your conscience is the arbiter of personal convictions. Convictions which may be of truth, or falsehood in the guise of facts or feelings. The law trains us, like the media tries to do.

Happy Birthday Évariste Galois

Happy Birthday Évariste Galois

A ‘few’ years ago, I ‘explored’ a mathematics seminar on Galois theory at university. Though they spoke English, I knew I would mostly hear mathematical Greek. But for a few Greek words that filtered into the English language, I would’ve left there grasping nothing but the atmosphere of mathematicians having high fellowship with one another; it would’ve been only an anthropological experience.

I recall Vague pictures of a four year old hand trying to span an octave while playing a piece on a piano. That was on CNN, years ago; a little maestro in the making. He made the octave in two steps, and it was beautiful. That wasn’t the ideal, or the perfect, but he went around the task excellently like children know to do well. He must have had, at the very least, a good teacher, and some motivation.

(And we praise children for their efforts, above and over the results they yield. As they grow older do we come to focus on results far above effort?)

“I had given to Moscow high school children in 1963-1964 a (half year long) course of lectures, containing the topological proof of the Abel theorem.” That was a statement by V. I. Arnold. These students, I suppose, were teenagers like Évariste when he started writing fantastic mathematical statements about our reality. A good teacher with the right perspective and proper organisation can teach some ‘high-end’ university level courses to high school kids.

High school is currently designed as a preparation and ‘selector’ for tertiary education. As currently formatted in Nigeria and many other countries, it has relatively little merit by itself. Enough university courses could be ‘downgraded’ to high school level when we think about it. Why not skip the ‘preparatory’ period, for amenable programs, and send the children straight to the degree.

If we say that high school education need not be a prerequisite for some university courses or degree programs, we mean, for example, that one could go from primary school to an MBA in six years tops. (Teeneage years better spent?) This is more easily workable if we have truly knowledgeable teachers; who can actually help the young ones learn, and who see and assert that high school students can handle more than the current standard.

Topology doesn’t sound like something that currently features in the regular high school curriculum. You’d more than likely find it at university only. But they can learn it and a few other big things earlier. It now perhaps depends on whether thats in a (direct) route to were they want to be.

Abraham Lincoln is said to have said that we’re only as happy as we want to be; it is in the same spirit to say that we’re only as knowledgeable as we want to be. But the right guidance and motivation is helpful and serves to accelerate progress. Kids would be smarter if we trained them to be smarter. (There’s a saying that an husband and wife parent a child, but the whole community raises him.)

Galois’ work in his early years are one reminder that teenagers could be trained to handle ‘much’ more than the certified curriculum designed for them. While Évariste was an outlier, that he did what he did as a teenager is telling. And there are many other examples. V. I. Arnold’s teaching Topology to high school kids says that it’s more a matter of organisation and presentation than difficultly for the age-group or grade.

Born October 25, 1811, he died about 20 years later with a legacy that was said would fill only 60 pages. For the significance of his works, Évariste Galois’ sixty pages were worth a PhD and more.

To find out more about Galois’ interesting life, visit: