Neural Network Learning Principles
of a recent string of educational fads which sought authority by claiming
(often without much justification) to be derived from neuroscience, many
educators now react badly to what they call 'neuro-babble', and have convinced
themselves that neuroscience has nothing to tell us, yet, about how we learn,
and therefore has nothing to contribute to the design of more effective
approaches to education.
It is true that neuroscientists don't say much about education, but it is not because they have nothing to contribute, it's because they are focused on a different set of problems and are working at a different level of detail. They are necessarily bogged down in the highly intricate scientific (experimental, statistical and ethical) problems that arise when they try to unravel such complexities as; why the speed at which we can read and distinguish between past and future tense verbs is effected by whether the words are presented in the left or right visual field?
tools and methods are designed to work at that small scale, the microcosm,
and are not suitable for exploring the general principles, the macrocosm,
of human learning. They know a lot about how our neural networks learn to
perceive the world, but they don't talk about it.
it falls to others (who like myself, are less constrained by professional/academic
group-think) to bridge the gaps between the different academic disciplines,
to extract principles from one domain and try them out in others.
of artificial intelligence software and smart robot-builders spend a lot
of time copying ideas and observations from neuroscience and working out
how they can be applied to real world problem-solving. From their perspective,
neuroscience has already discovered many new and fundamental principles
of neural network learning which have profound implications for the
design of human education systems. Here are few particularly important ones.
In evolutionary terms, high quality sensory information is a valuable but expensive commodity. A balance had to be struck. As a consequence, our sensory capability is surprisingly limited, much more limited than we normally realize. We feel as if we can see everything there is to see, and hear everything there is to hear, but actually we can only detect a tiny proportion of the vast amount of information that is bouncing around the universe and we are deaf and blind to the rest of it.
But evolution is an awesome problem-solver and it came up with a very smart solution.
brain uses this trapped experience to pre-consciously enhance our limited
real-time sensory information. Your understanding of how the world appeared
to work in the past, (as encoded in the structure of your neural connections)
is used to help make sense of the limited, vague, ambiguous and noisy
information you are receiving from your senses right now.
It is important to understand how neural networks trap and use past experience.
accumulated maps and models about the world are not stored as facts or data
in a lookup table, they are 'encoded' in the current wiring of the network
which continuously evolves in response to the sensory experiences that flow
through it. New real-time sensory information passes through (selectively
stimulates and is interpreted by) networks which have been shaped by past
experience, networks which have been 'taught' by experience to be sensitive
to particular patterns of associations and distinctions, and insensitive
to others. The old term 'encoding' is too static and fails to capture the
continuous perceptual evolution made possible by this neural network
is how we interpret our environment, how we recognize familiar objects,
people, places, movements, smells, situations, threats and opportunities,
from within the stream of information that flows through our senses. This
is how past experience is pre-consciously employed to help make sense of
limited, noisy, current information.
Our perceptions, judgments and reactions, feel much more solid than they really are. They are actually composed of a small measure of sensory information and a large measure of assumptions about how the world 'is' now, which are based on our experience of how the world appeared to us in the past. The quality of our current perception, our understanding and our behavioural choices is highly dependent on the quality of our accumulated maps and models about reality - and the quality of our maps and models changes significantly with time and experience.
Neural network learning curves.
are so confident in the truth of our current model of the world that we
are not normally aware of the profile of our own learning curve. Adults
forget - to use one of Piaget's examples - that there was a time in our
childhood when we didn't know that 'number is conserved'. Then, one day
we discovered, to our astonishment (amusement, delight), that counting a
row of ten pebbles from left to right produces the same result as counting
them from right to left. Adults forget that we had to learn how to see and
experience the world, and we forget that the experience of learning, finding
new ways to make sense of our world, is very good fun.
are also blissfully unaware of all the things that we are not yet aware
of. Gradually, step by step, we become aware of, learn to distinguish between,
more and more different aspects of the world. Once we start paying attention
to something new, we learn fast, but as our maps and models improve, our
attention reduces. We have the feeling that we know all about that now,
and we only pay attention if something unexpected/unexplained happens. And
then, too often, learning grinds to a complete halt and we enter the rigid
belief phase, where our curious learning attention is no longer activated
by that topic.
even the arrival of some new experience that (to an independent observer)
highlights glaring inconsistencies in our maps and models, is enough to
shake our belief and trigger a review and update of our understanding. This
is not because we are stupid, illogical or irrational, it is because our
neural networks have settled into such a stable state that they can no longer
detect, respond to, or learn from, these new inconsistencies.
so it goes - we stumble across our own individual cognitive landscape, from
the blissful ignorance of inexperience, to the self-assured ignorance of
too much experience, via a trail of temporary errors. But this neural learning
mechanism works very well, most of the time. Well enough to have enabled
an unbroken chain of our ancestors to cope with a huge variety of very challenging
situations. Their struggles ensured that we, their offspring, inherited
a neural platform that was very well adapted to working out how to cope
with the types of problems that arise for a human being on the surface of
planet earth. We are naturally very good at learning from experience and
applying that experience to the business of staying alive. What we need
is a rich supply of the right types of experiences to learn from, and
a healthy cultural framework to tell us how to make sense of the experiences.
Structurally encoded experience-trapping is the crucial concept here.
order to understand how this experience-trapping is possible, you first
need to understand both the concept and the mechanisms of neural plasticity.
This will be a new and unfamiliar concept for most people because most of
the machines and systems we encounter in everyday life do not work in this
naturally assume that the electrical wiring in our radios, DVD players,
toasters, and washing machines, will remain the same from one day to the
next, and that these pieces of equipment will behave in exactly the same
way tomorrow as they did today. We do not expect that the wiring will rearrange
itself every time it is used, and that its behaviour
will evolve as a result. But that is what happens in our neural networks.
The wiring in a neural network subtly rewires itself every time it is used,
adapting and fine tuning its performance, growing new connections to speed
up the reaction times of busy circuits, connecting subsystems that repeatedly
get used at the same time, re-prioritising menus, adjusting switches and
sensors to make them more or less sensitive, cutting away and recycling
redundant wiring that hasn't been used for a long time.
There are some everyday examples of plasticity (things changing their structure as a result of experience) that may help in developing an understanding of these experience-trapping processes at work in our neural networks.
an example of a sequence of events causing structural changes in a system,
which then influence the future behaviour of the system, think of rain falling
on a hillside. The water flows downhill and collects at the lowest point
to form a little lake. The level in the lake rises until the water overflows
its retaining barrier and forms a powerful stream which washes away the
land surface to cut a passage across the landscape to the next lowest level
lake. Every time it rains the whole system evolves a little bit, and the
flowing water cuts a slightly different and more direct path to the sea.
Once a river system is well established, it is very difficult for a new
river system to get started in that area, because the rain naturally flows
into the existing system. Similarly, patterns of connections form in our
neural networks, as a result of the sequence of experiences that have flowed
through that area of the brain. The current state of these patterns determines
how we will respond (interpret, perceive, react) to the next experience,
and that new experience will play its part in further fine tuning the overall
a well established river system, a well established neural pattern comes
to dominate its landscape. Whilst it may still be able to adjust itself
in response to trivial and fluffy new experiences at the edge of its territory,
its core structures may become so well established that they become very
insensitive to important new evidence (the belief stage). An important new
experience might be able to start a new furrow in an unploughed region of
the network but it probably won't have any effect on the core structures
in an established pattern.
an example of a more dynamic network plasticity, think about the evolution
of a group of friends on a social networking website. Suppose one member
of an existing group develops a new interest and tells his/her group friends
about it. Some of them are interested in this new topic, others are not.
The ones who are interested get excited about the new subject, and start
telling other people, some of whom are not part of the original group.
Some of these outsiders then join the original group, bringing new ideas
and associations with them. This changes the nature and balance of the
original group. As a result, the original group starts to split into two
different groups with slightly different characteristics; one centered
around the members who were interested in the new idea, and the other
centered around the ones who were not. Because the two groups now have
slightly different characteristics, they may react quite differently to
the next new idea that comes along. This is quite a good model for neural
network plasticity: a dynamic structure whose reactions to external events
change over time, because its internal structure is constantly being reshaped
by the flow of resonant experiences.
So what can these neural network principles tell us about human learning and teaching.
Be aware of the weaknesses in the neural network mechanism.
Important - Our neural networks are very good at detecting local 'cause
and effect' relationships. They can detect and store associations between
things which happen close together in both space and time. We are very bad
at detecting remote associations where the cause and effect are separated
by either time or space. To ease our anxiety and reduce uncertainty, we
invent reasons to explain remote causation. We have a long history of invented
reasons to explain why droughts, floods, earthquakes, plagues, etc., occur.
our neural network understanding is based on experience, we are very bad
at assessing the probability of things we have not yet experienced. We do
not expect the unexpected. We are not good at detecting random chaos, so
we tidy-up and simplify the past, which causes us to underestimate the amount
of sudden change there is in our environment. We assume the world is more
stable than it is. We assume it has always been the way it is now. We get
anxious if it looks like changing. We don't understand that it is and has
always been changing. (This perception is more prominent in western culture
which developed the idea that God would make the world ok for us if we behaved
properly - eastern religions have always known the world is constantly changing.)
think / feel that our perception of reality is true. We don't realize that
we invented our current interpretation of reality and that our perception
is very limited. We have to learn by experience that the world looks very
different from other people's points of view, and from within other cultural
or ideological frameworks.
often distort our own experience-based perception and models of reality,
in order to fit in with the orthodox views of significant social groups.
Particularly if there are rewards and punishments involved. So watch out
for the influence of fashion and group-think. Many of our attention mechanisms
operate pre-consciously in the vast submerged iceberg of the mind. Advertisers,
and persuaders of all types, will try to hijack these attention mechanisms
to get you to buy their products or lend support to their causes. Be aware.
|And finally - our neural networks evolved to grow up in a more natural setting than the modern urban landscape most of us now inhabit. It remains to be seen how well they will function in this new setting.|