There is a passage in On intelligence about the differences between parallel processing in human versus computers :
From the dawn of the industrial revolution, people have viewed the
brain as some sort of machine. They knew there weren't gears and cogs
in the head, but it was the best metaphor they had. Somehow
information entered the brain and the brain-machine determined how the
body should react. During the computer age, the brain has been viewed
as a particular type of machine, the programmable computer. And as we
saw in chapter 1, AI researchers have stuck with this view, arguing
that their lack of progress is only due to how small and slow
computers remain compared to the human brain. Today's computers may be
equivalent only to a cockroach brain, they say, but when we make
bigger and faster computers they will be as intelligent as humans.
There is a largely ignored problem with this brain-as-computer
analogy. Neurons are quite slow compared to the transistors in a
computer. A neuron collects inputs from its synapses, and combines
these inputs together to decide when to output a spike to other
neurons. A typical neuron can do this and reset itself in about five
milliseconds (5 ms), or around two hundred times per second. This may
seem fast, but a modern silicon-based computer can do one billion
operations in a second. This means a basic computer operation is five
million times faster than the basic operation in your brain! That is a
very, very big difference. So how is it possible that a brain could be
faster and more powerful than our fastest digital computers? "No
problem," say the brain-as-computer people. "The brain is a parallel
computer. It has billions of cells all computing at the same time.
This parallelism vastly multiplies the processing power of the
biological brain."
I always felt this argument was a fallacy, and a simple thought
experiment shows why. It is called the "one hundred–step rule." A
human can perform significant tasks in much less time than a second.
For example, I could show you a photograph and ask you to determine if
there is cat in the image. Your job would be to push a button if there
is a cat, but not if you see a bear or a warthog or a turnip. This
task is difficult or impossible for a computer to perform today, yet a
human can do it reliably in half a second or less. But neurons are
slow, so in that half a second, the information entering your brain
can only traverse a chain one hundred neurons long. That is, the brain
"computes" solutions to problems like this in one hundred steps or
fewer, regardless of how many total neurons might be involved. From
the time light enters your eye to the time you press the button, a
chain no longer than one hundred neurons could be involved. A digital
computer attempting to solve the same problem would take billions of
steps. One hundred computer instructions are barely enough to move a
single character on the computer's display, let alone do something
interesting.
But if I have many millions of neurons working together, isn't that
like a parallel computer? Not really. Brains operate in parallel and
parallel computers operate in parallel, but that's the only thing they
have in common. Parallel computers combine many fast computers to work
on large problems such as computing tomorrow's weather. To predict the
weather you have to compute the physical conditions at many points on
the planet. Each computer can work on a different location at the same
time. But even though there may be hundreds or even thousands of
computers working in parallel, the individual computers still need to
perform billions or trillions of steps to accomplish their task. The
largest conceivable parallel computer can't do anything useful in one
hundred steps, no matter how large or how fast.
Here is an analogy. Suppose I ask you to carry one hundred stone
blocks across a desert. You can carry one stone at a time and it takes
a million steps to cross the desert. You figure this will take a long
time to complete by yourself, so you recruit a hundred workers to do
it in parallel. The task now goes a hundred times faster, but it still
requires a minimum of a million steps to cross the desert. Hiring more
workers— even a thousand workers— wouldn't provide any additional
gain. No matter how many workers you hire, the problem cannot be
solved in less time than it takes to walk a million steps. The same is
true for parallel computers. After a point, adding more processors
doesn't make a difference. A computer, no matter how many processors
it might have and no matter how fast it runs, cannot "compute" the
answer to difficult problems in one hundred steps.
So how can a brain perform difficult tasks in one hundred steps that
the largest parallel computer imaginable can't solve in a million or a
billion steps? The answer is the brain doesn't "compute" the answers
to problems; it retrieves the answers from memory. In essence, the
answers were stored in memory a long time ago. It only takes a few
steps to retrieve something from memory. Slow neurons are not only
fast enough to do this, but they constitute the memory themselves. The
entire cortex is a memory system. It isn't a computer at all.
The point made here is that the computing paradigm (that is, the way the whole thing works) of the brain and the computer are completely different. The computer is a Turing machine, and the brain is something else, possibly a memory system if you think that Jeff Hawking is right. Whatever it is, the brain is not a Turing machine.
To go back to your question:
Why can't human brains be used to do massive parallel processing in
the same way computers are doing today?
It has to do with the way the human brain works. If you assume that the brain will do any task in a parallel fashion, and the more neurons involved, the better the performance; then in order to maximize your performance you should use your whole brain. 1 task: 100% performance, 2 tasks: 50% performance, 3 tasks: 33% performance, and so on.
But if you add an "attention switching cost" to go from one task to another, then you are better off just focusing on one task where the switching cost is zero.
So you can multitask, but it won't be efficient.
