Mind Hacks™: Tips & Tools for Using Your Brain (43 page)
Read Mind Hacks™: Tips & Tools for Using Your Brain Online
Authors: Tom Stafford,Matt Webb
Tags: #COMPUTERS / Social Aspects / Human-Computer Interaction
Your memories aren’t stored discretely like objects in a filing cabinet; rather, they
are interleaved with other things in memory. This explains why you’re good with faces but
not with names, why you should go back to your hometown to better remember your school
days, and maybe even why you dream, too.
Human memory is not organized like a filing cabinet or a hard disk drive. In these
storage systems, each memory is neatly indexed and stored so that it doesn’t affect any
other memory. The items in a computer memory don’t affect processing unless they are
explicitly retrieved, and to retrieve them, you have to consult an index to work out where
they are. If you don’t know where they are or if you don’t have the right tag by which to
access the files, you’re out of luck — you’re stuck with a brute force look through each file,
one by one. The same holds for finding related items — you do it through some form of indexing
system or again resort to a brute-force search. The system is content-blind.
But human memory is even further unlike any filing cabinet or computer memory system.
This is the fundamental difference:
human memories are stored as changes in the
connections between neurons, the self-same neurons that actually do the
processing
.
So there are no passive storage locations: the processing-storage distinction
fundamental to conventional computer architecture
1
doesn’t hold. Instead, memories about things are stored by the same units
that are responsible for processing them. As you look at a face, your brain doesn’t need to
send away for information on whether you’ve seen the face before, and it doesn’t need to
store or index that face so that it can be recognized later. The ease with which that face
was processed by your neural units provides a signature that can be used to calculate
familiarity
[
Fake Familiarity
]
. If you
see the face
once, it makes it easier for the neurons that respond to that particular
combination of features to respond together, effectively acting as a key for recognizing it
later.
So it should be clear why recognizing faces is easier than recalling names. When
recognizing faces, your brain is presented with some input (a face) and can tell if it is
familiar just by checking whether the neurons for representing that face easily
coactivate. (If all the neurons representing a face activate together easily, that means
they’ve activated together in the past. That is, you’ve seen the face before.) For
recalling the name, you have to recognize the face and then hope that the association with
the word information you heard at the same time (the name) as you first met the face is
strong enough to allow that to be activated. It’s a different process (recall versus
recognition) in a different modality (image versus words); no wonder it’s so much
harder.
Of course, if human memory
were
organized like computer memory,
then recognizing faces would be an equivalent task to recalling names. Both would involve
checking the input (the other person’s face) against everything you’ve got stored. If
you’ve got the face stored, bingo! — you recognize it. And the information you retrieve to
recognize it would be automatically linked to the name, so recalling the name would be
just as easy as recognizing the face. Unfortunately, on the flip side, recalling a face
would be just as difficult as recalling a name.
— T.S.
The second important consequence of this fundamental difference is that memories are
distributed between many neurons, all of which are involved in storing many other memories.
This means that memories aren’t stored independently of one another; so, learning something
new can interfere with your memory of something old (and, of course, the things you already
know affect what you remember about new material).
Forgetting something isn’t just a matter of information falling out of your brain, as
if your brain were a filing cabinet turned over. Traces remain of any information that is
forgotten. This is why relearning things is easier than learning them for the first time.
And because memories are fundamentally entangled with one another, remembering or
relearning something brings related memories closer to the surface too.
One way of showing this is to relearn a subset of some set of knowledge that you have
previously learned and forgotten. The effect of relearning should transfer to other
memories in the same set,
2
benefiting all the associated memories, not just the ones you deliberately
relearn.
The vocabulary of another language is a good example of something that you
learn and use all together, and then forget. In my case, I’ve forgotten the French I
learned at school. So, to demonstrate to myself that the entanglement of memories would
produce a transfer effect I performed the following experiment: I took a list of 20 common
verbs
and tested myself to see how many I could remember the French
for. It turned out I could remember the translation for 8 words. I then found out what the
remaining 12 French words were. This was the relearning phase. If I wanted to be more
thorough, I could have relearned some nouns and adverbs as well, but I didn’t. Next, I
tested myself on 20 common
adjectives
. This time I got 13
English-to-French translations right. After only a few minutes thinking about French again
it was coming back to me — I was more than 50% better at the second set of words, despite
being just as unpracticed at these as the first set. Retrieving one set of French
vocabulary from my memory had strengthened the associations required for me to recall more
and more.
Think of the fundamental currency of memory as being associations, not items. This is
core to the design of the whole system. It means that content can be accessed by anything
associated with it, not just any single arbitrary tag. Human memory is
content-addressable. This is unlike computer memory, where you access stuff only through
the arbitrary tags you use to keep track of information and that you decide on at storage
time (like filenames). The reason Google is so popular is that it gives us
content-addressable memory for the Internet. You can type in pretty much anything you
remember about the contents of a web page and it comes up in the results. And, also like
the Internet, much of the meaning of memory is to be found in the connections and
associations, which shift and recombine separately from the content.
A famous psychology experiment involved divers learning lists of words either on the
docks or underwater and then being tested either on the docks or underwater.
3
Those who scored highest on the test were those who were tested in the same
situation in which they learned the material (i.e., tested underwater if learned
underwater, tested on the docks if learned on the docks). Those who scored lowest were
those who switched contexts from learning to recall. This demonstrates the automatic
encoding of context along with memories
[
Change Context to Build Robust Memories
]
and provides some
justification for the advice I was given as a student that if you learned something when
drunk you should go into the exam drunk. (It may well be true, but it might also be better
not to have been drunk in the first place.) Being able to recall better in the original
situation is one consequence of your context being automatically laid
down in memory. Another consequence is the transfer effect, as shown in the
preceding
In Action
section: memories are tangled up with other
memories of the same type and themselves constitute a kind of memory context. Remembering
one set of knowledge puts you in the right context, and the associated memories follow
more easily.
The need to interleave many different memories in the connections between neurons may
be one of the functions of sleep. It’s vital to store new memories in the same networks of
association as used by old memories, otherwise you’d have no way of moving between them.
But at the same time, it’s important not to overwrite old memories. There is evidence that
the need for this process, called interleaving, may explain some features of our memory
systems, and there’s also evidence that it may occur during sleep as dreams or part of dreaming.
4
- The Von Neumann architecture separates processing from data and code (
http://en.wikipedia.org/wiki/Von_Neumann_architecture
). - Stone, J. V., Hunkin, N. M., & Hornby, A. (2001). Predicting
spontaneous recovery of memory.
Nature, 414
, 167–168. - Godden, D., & Baddeley, A. (1975). Context-dependent memory
in two natural environments: On land and underwater.
British Journal of
Psychology, 66
(3), 325–331. - McClelland, J. L., McNaughton, B. L., & O’Reilly, R. C.
(1995). Why there are complementary learning systems in the hippocampus and neocortex:
Insights from the success and failures of connectionist models of learning and memory.
Psychological Review, 102
, 419–457
You can train your strength and skill with imagination alone, showing that there’s a
lot more to limb control than mere muscle size.
How your brain controls your muscles is something you don’t notice until it goes wrong.
When you drop a plate for no good reason, when disease or age rob you of the ability to will
your muscles to move just like that, when you can’t stop your legs trembling (even though
that is possibly the least useful thing they could be doing in your situation),
then
you notice the gap between what you want to happen and what your
muscles do. Normally the coordination of body movement happens so smoothly and (seemingly)
instantaneously that it’s hard to really believe there are any gaps in these processes. Hold
your finger up in front of your face. Watch it carefully. And...ready...
curl it. Magic. How did that happen? It’s impossible to truly introspect about
the control system involved: our bodies appear to be the ultimate pieces of invisible
technology.
But that doesn’t mean there isn’t a very complex system of control in place. It needs to
be complex for the range of jobs done, at the speeds they’re done. The standard
visuomotor feedback loop
(the delay between acting and getting visual
information to update or correct that action) is 100–200 milliseconds,
1
so much of this control has to happen without the aid of direct guidance from
the senses. Movement must be controlled, at least in part, by processes that do not require
immediate sensory feedback.
There’s that number again: 100–200 ms! It occurs all over this book, and I think this
may be the root of it; the commonly found window for conscious experience [
Show Motion Without Anything Moving
] may be this size because of the
uncertainty introduced by the delay between our senses and reactions. So this is the range
over which our brain has developed the ability to predict, by simulation, the outcome of
our actions.
— T.S.
The thing is, movements are often so quick it doesn’t feel as if feedback loops are
intimately responsible. Rather, it often
does
feel as if you send a
“go” signal to your hand to stretch a finger or catch a ball. So how can we show this is
actually what’s happening? One way is to work on developing the control system itself and
see how that influences the resulting movement. If these systems do indeed exist, then
developing them without simultaneously developing your muscles should still improve
performance.
Using your imagination alone you can train the motor signals from your brain so that
you are stronger, faster, and more skillful. This example takes 3 months to work, so you
may want to just listen to how the experiment was done rather than doing it yourself. It’s
taken from a study led by Vinoth Ranganathan,
2
who was following up on a study done 12 years previously by Guang Yue in
the Lerner Research Institute department of biomedical engineering.
3
The study involved volunteers training, in two different ways, the muscle responsible
for pushing outward the little finger. (To see what they were doing, put your hand palm
downward on the table, fingers together, then imagine you’re pushing a weight out by
moving your little finger only to the side). They trained for 12 weeks, 15 minutes a day,
5 days a week. Some volunteers
trained by actually tensing the muscle, but others were instructed to merely
imagine
doing so.
After 12 weeks, Ranganathan measured the force that the volunteers could exert with
their little finger muscle. Both groups had become stronger, those actually tensing their
muscles during training improving by 53%, those using imagination by 35%. That’s not a
large gap, especially if you consider that training just using your imagination is
probably the harder task to do.
The Ranganathan study used the little finger muscle because it is not used much. It is
easier to see changes in strength here than in more primary muscles, such as those in the
arms or legs.
In the same study by Ranganathan et al., another group of volunteers showed they
could increase the strength of a more important muscle — their elbow flexor — using just
mental training too.
As well as measuring the force exerted by the volunteers before and after training,
the researchers also measured the control signals sent by the brain to the muscle using
EEG
[
Electroencephalogram: Getting the Big Picture with EEGs
]
and other measures. They were able to conclude that the main reason for
the increase in strength was an increase in the strength of the signal from the motor
regions of the brain to the muscle, not an increase in the size and strength of the
physical muscle.
This fits with other findings, including one that training muscles on one side of the
body can increase the strength of the corresponding muscle on the untrained side.
The major part of any initial improvement in muscle control may be getting the signal
right, rather than training the muscle. Correspondingly, the contextual interference
effect — practicing skills in a random order is a better way to improve performance
[
Change Context to Build Robust Memories
]
— has been shown to work with mental practice too.
4
There are three classes of control system used to moderate movements while they occur,
and these are used in situations from needing to move your arm more to catch a ball in a
high wind to your legs changing their walking pattern onboard a ship.
Feedback
- All neural systems include some noise
[
Neural Noise Isn’t a Bug; It’s a Feature
]
, so even if your
movements are planned correctly (you calculated the right amount of force to apply,
etc.), your brain needs to check they are not going off course and reset them if they
are. You are trying to catch a ball and realize your hand is out of place, so, while
you’re moving it toward the ball, you speed up your movement so it gets to the right
place in time. An additional complication is learning movements across trials, when
you know what you want to do (juggle three balls, for example) but you have to train
your movements to successively improve each time you try.
Feedforward
- A feedback system can work in isolation, detecting error and compensating for its
effect. In comparison, feedforward systems use information from a component that may
introduce error to anticipate the error itself. This component sends information ahead
to whatever has to deal with the potential difficulty so it can be accommodated for.
For example, the vestibular-ocular reflex
[
Understand the Rotating Snakes Illusion
]
translates head
velocity into compensatory eye velocities. Head movements introduce distortions into
vision, so the feedforward mechanism notices the head motion and triggers eye
movements to cancel out any motion blur before it even occurs.
Forward modeling
- Some movements need correcting during their execution at a rate quicker than is
possible with simple feedback. One way of doing this is to make a prediction of what
the effect of a signal from your brain to your muscles will do (as described in
Why Can’t You Tickle Yourself?
). The prediction can then be
used as pseudofeedback to control movements at a speed faster than would be possible
with actual sensory feedback. Forward modeling allows batters to hit baseballs (or
batsmen to hit cricket balls, depending on your preferred game) thrown at them at
speeds faster than their simple sensory systems should allow them to deal with. This
system also has advantages over feedback because of the difficulties that occur when a
feedback signal is delayed. A late feedback signal means it’s actually responding to a
situation now past in which the error was larger, so the correction applied can cause
an overshoot and lead to oscillations around the correct position rather than an
iteration toward it (although introducing a damping factor — an automatic reduction of
the delayed feedback signal — can compensate for this).
So movement control is more complex than it might at first seem. Making a muscle
movement isn’t as simple as sending it a simple “go” or “don’t go”
signal and letting ballistics (launch it, let it go) take care of the rest.
Movements have to be controlled while they’re in action, and the best control mechanism of
the three in the list depends on the characteristics of the system: that is, how long it
takes for information to influence action.
It isn’t just strength that can be trained, but coordination as well. I once practiced
with a very senior judo instructor who told me that an hour’s worth of going through judo
techniques in the imagination was as good as an hour’s worth of actual training. I was
skeptical at the time, but research seems to confirm his suggestion. For example, mental
rehearsal of a piano sequence results in similar levels of improvement (and similar
strengthening of cortical signals) as actual practice.
5
So if you can’t get to the gym, put aside some time for mental rehearsal of your
exercises. You won’t lose any weight, but you’ll be better coordinated.
- Jordan, M. I. (1996). Computational aspects of motor control and
motor learning. In H. Heuer & S. W. Keele (eds.),
Handbook of
Perception and Action
. New York: Academic Press. - Ranganathan, V. K., Siemionow, V., Liu, J. Z., Sahgal, V., &
Yue, G. H. (2004). From mental power to muscle power — gaining strength by using the
mind.
Neuropsychologia, 42
, 944–956. - Yue, G. H., & Cole, K. J. (1992). Strength increases from
the motor program: Comparison of training with maximal voluntary and imagined muscle
contractions.
Journal of Neurophysiology, 67
, 1114–1123. - Gabriele, T. E., Hall, C. R., & Lee, T. O. (1989). Cognition
in motor learning: Imagery effects on contextual interference.
Human
Movement Science, 8
, 227–245. - Pascual-Leone, A. et al. (1995). Modulation of muscle responses
evoked by transcranial magnetic stimulation during the acquisition of new fine motor
skills.
Journal of Neurophysiology, 74
(3), 1037–1045.
