is a gigantic project yet to be done that will have the
effect of rooting psychology in natural science. Once
this is accomplished, you'll be able to go from phenomenology.
. . to information processing. . . to the brain. . . down
through the workings of the neurons, including the biochemistry,
all the way to the biophysics and the way that genes are
up-regulated and down-regulated.
This is going to happen; I have no doubt at all. When
it does were going to have a much better understanding
of human nature than is otherwise going to be possible.
SHAPE ARE A GERMAN SHEPHERD'S EARS?: A TALK WITH STEPHEN
When Stephen Kosslyn received tenure at Harvard, none
of his colleagues in the Psychology Department had scholarly
interests that overlapped with his, since most people
were doing mathematical psychology. Prior to Harvard,
during his time at Johns Hopkins, Kosslyn had become very
interested in the brain and computation, which was the
beginning of cognitive neuroscience. There weren't too
many people thinking about such matters at that point.
Over time, many of his senior colleagues in the Psychology
Department at Harvard retired or left, so he found myself
in the position of being chair of several search committees,
where he could nudge the program in a direction that,
he believes, turned out to be a very good idea. Kosslyn
chaired the committee that hired Dan Schachter, Patrick
Cavanaugh, Ken Nakayama, and Alfonso Caramazza. "I
tried to get Pinker, but failed on that one for
now," he says. "Most recently I chaired the
committee that brought in Susan Carey and Liz Spelke.
The department's gotten strong now. It's got a cohesive,
underlying theme, which means that there is the potential
The Department is currently oriented towards cognitive
neuroscience. "Right now," according to Kosslyn,
"its not very computational, which is a weakness.
Computation is the language of information processing,
not English, French, or any other natural language because
theres no reason to expect the kinds of concepts
and distinctions captured in natural language to be appropriate
for characterizing what's going on in the brain. It's
different than the objects we encounter in our daily lives.
Although we don't have the right version of a computational
language yetone thats tailored for this particular
machine rather than a Von Neumann machinecomputation
is clearly going to be the language."
M. KOSSLYN, a Professor of Psychology at Harvard University,
has published over 200 papers on the nature of visual
mental imagery. He has received numerous honors, including
the National Academy of Sciences Initiatives in Research
Award and the Prix Jean-Louis Signoret, and was elected
to the American Academy of Arts and Sciences and the
Society of Experimental Psychologists. His books include
Image and Mind; Ghosts in the Mind's Machine; Elements
of Graph Design; Wet Mind: The New Cognitive Neuroscience;
Image and Brain: The Resolution of the Imagery Debate;
and Psychology: The Brain, the Person, the World.
Kosslyn is a Fellow of the American Psychological Association,
the American Psychological Society, and the American
Association for the Advancement of Science, and has
served on several National Research Council committees
to advise the government on new technologies. He is
also co-founder of the Journal of Cognitive Neuroscience.
STEPHEN KOSSLYN: For the last 30 years Ive been
obsessed with a question: What shape are a German Shepherds
ears? Of course, I'm not literally interested in that question,
since if I were I could just go out and look at dogs; Im
really interested in how people answer the question from memory.
Most people report that they visualize the dogs head
and mentally "look at" its ears. But what does it
mean to visualize something? What does it mean to "look
at it in your mind"? It's a bit absurd, because there
can't be a little man in there that is actually looking at
a picture. If there were, there would have to be a little
man inside that man's head, and so forth, and it doesn't make
For many years we tried to collect objective evidence to show
that when you have the experience of visualizing, theres
actually something pictorial in your head. It turned out that
the best way to approach this was by turning to the brain.
There are parts of the brain that are physically organized
such that when you look at something, a corresponding pattern
is physically laid out on the cortex. Even the first visual
area in the processing stream is often activated during visual
imagery even if your eyes are closed when you visualize.
Moreover, the way it's activated depends on what youre
visualizing. If you visualize something thats vertical,
you find activation along the so-called vertical meridian
; if its horizontal, the activation flips over on its
side. Its absolutely amazing. Similarly, visualizing
objects at different sizes changes the pattern of activation
in ways very much like what occurs if you are actually seeing
objects at the corresponding sizes.
But Ive been working on this for over 30 years now and
I want to move on. Instead of trying just to establish that
there actually are mental images and that these images are
bona fide representations that have a functional role in processing
systems, I want to ask: So what? Who cares? Why should my
mother be interested in this kind of thing?
Lately Ive been working on something that I'm tentatively
calling the "Reality Simulation Principle." It is
built on my lab's findings that about two-thirds of the same
brain areas are involved in visual mental imagery and visual
perception. This finding occurs even when the tasks seem very
different on the surface (for example, visualizing an upper
case letter in a grid and deciding whether an X mark would
fall on the letter if it were actually in the grid versus
deciding whether a spoken name is appropriate for a picture).
This is a huge amount of overlap, which leads us to suspect
that an object seen in a mental image can have the same impact
on the mind and body that the actual object would have. My
notion is that once the brain systems are engaged, they don't
know where the impetus came from. This means that they can
produce the same effects whether you activated it endogenously
(from information in memory) or exogenously (from looking
The "Reality Simulation Principle" describes how
to use mental images as stand-ins for actual objectsto
manipulate yourself, basically. It is useful to understand
it in conjunction with what I call the GITI cycle, which stands
for Generate, Inspect, Transform, Inspect. If mental images
can simulate or stand in for actual objects and scenes, you
can generate the image, inspect what youve got, transform
it, and inspect the result. This can be done iteratively,
meaning that you can use imagery to take advantage of the
"Reality Simulation Principle"to do all sorts of
good things for yourself.
What kinds of good things am I talking about? Memory is one
obvious example. From the work of Alan Paivio and countless
others, we know that youre able to remember objects
better than pictures of objects, and pictures of objects better
than words. It also turns out that if you visualize the objects
named by words you do better than you would otherwise. Consequently,
we're interested now in things like hypnosis. We can hypnotize
you, have you visualize an object, and imagine that its
actually a three-dimensional object, appearing in glorious
vivid detail. In this case, your memory would be boosted even
Mental practice is another candidate. Neuroscientists such
as Marc Jeannerod and Jean Decety have shown that imagining
doing something recruits most of the brain mechanisms that
would guide the corresponding actual movements. And people
in sports psychology have shown that by imagining that youre
engaging in some activity youll actually get better
at doing it. This process involves generating an image, inspecting
the image, transforming it by imagining your movements, seeing
what the result would be, and then cycling through again.
The next time through you can change the image as a function
of the result you saw. If you imagine youre playing
golf, for example, and your ball doesnt get in the hole,
you can imagine what would happen if you whacked it a little
more softly. Mental practice clearly works. By understanding
how mechanisms of imagery works we can actually optimize this
The "Reality Simulation Principle" can also be used
to acquire self-knowledge. Try this one out. Imagine its
dusk, youre walking alone, and youre late. You
start to walk faster and then notice a short-cut through an
alley. Its getting a little dark, but you really dont
want to be late, so you start to go towards it, and you notice
that there are three guys lingering near the mouth of the
alley. Now think about a first scenario: The three guys look
like theyre 20 years old, are wearing long droopy shorts,
dirty t-shirts, baseball caps that are on backwards, and are
smoking cigarettes. As you get close, they stop talking, and
all three heads swivel and fixate on you and start tracking
you. How do you feel?
Now try the same thing, except instead of those three guys,
make them three balding middle-age, overweight accountants
wearing suits. Theyre standing there smoking cigarettes,
and their heads swivel as they track you. How do you feel
You can start simulating the effects of different attributes.
For example, what if the guys are black or Latino teenagers.
How do you feel? If you can start actually sorting out your
own emotional landscape by running these kinds of mental simulations,
you may, in fact, discover certain things about yourself that
may be surprising.
Some people who confront their own beliefs may think theyve
got some racial issues, and it may turn out theyre actually
class issues. Make those middle-aged accountants black and
see how you feel. Those kinds of simulations can help produce
self-knowledge, and can help a person to improve his emotional
You can also manipulate your body. Its obvious that
if you have a sexual fantasy you manipulate your body by imagery.
Also, if you imagine something scaryan anticipated encounter
with an authority figure or a walk along a narrow path in
the mountains that is starting to crumbleyour palms
will sweat and your heart beat will change. Its clear
that mental imagery can affect the body, but it turns out
that it may be more interesting than that. For example, one
of the things were studying now is how to change your
hormonal landscape by manipulating your images.
There's something called the victory effect, where if you're
a male and you win some sort of contest your testosterone
goes up afterwards. If you lose, it goes down. This is not
a surprise. It also turns out that if you watch your favorite
team win your testosterone will go up. If your team loses,
itll go down. This even works if youre watching
chess, so its not about being aroused. In fact, it works
for the chess players and for the people who are watching
Why is this interesting? With men it turns out that spatial
abilities vary as a function of testosterone levels. In the
fall, males testosterone levels are relatively high.
They go down thereafter, and then they pick up again. Much
research suggests that the relation between testosterone levels
and spatial abilities is a U-shaped function; your spatial
abilities are not as good if you have too much testosterone
or too little testosterone. As you get older, both testosterone
levels and spatial abilities drop. There is a lot of evidence
that there is a connection between the two. The question is,
can we manipulate ones spatial abilities by having you
run simulations of watching yourself win or lose? If "Reality
Simulation Principle" is correct, manipulating your own
testosterone levels would in turn affect your spatial abilities.
This is work in progress in my lab, in collaboration with
Peter Ellison and Carole Hooven; stay tuned.
My point is that you can use "Reality Simulation Principle"
in lot of different ways, including some ways that are not
intuitively obvious, such as manipulating your hormonal landscape.
Mental imagery is also important in creativity and problem
solving. Einstein reported that most of his thinking was done
with images prior to any kind of verbal or mathematical statement.
We know quite a bit now about how to use images in the service
of solving problems and being creative. In fact, Ron Finke
has written a couple of unusually creative books on this topic.
Other people have also claimed that you can even manipulate
your health by using what I'm calling the "Reality Simulation
Principle". Im a little skeptical about this. We
may look at that eventually, but not right now. It's certainly
the case that you can manipulate the placebo effect to some
extent, but the medical effects of "Reality Simulation
Principle" are probably not huge. It's not going to cure
My premise has always been that the mind is what the brain
does. Of course, thats a little too glib; really the
mind is what the cortex does, since the brain does things
like respiration that are not mental. If this is the case,
then the question becomes, how do we understand information
processing in the brain?
Switching topics, let's return to the role of computation
in all of this. The computer is convenient because it allows
us to think about how events at different levels of analysis
can interact. This is one of the deepest questions in psychology,
and probably science in general. It's really a mystery. How
is it that semantics and the meaning of things dictate a sequence
of events in this wet machine? The wet machine itself has
neurons, each of which have an average of ten thousand connections.
Sure it's complicated, but ultimately you can understand the
whole thing in terms of chemistry and physics.
But how does this machine produce semantically interpretable,
coherent sequences of activity, and allow these activities
to be modulated by the semantics of what it registers from
the world? When you say something to me, its obviously
not just sound patterns, since the content influences what
my brain is doing. How Im going to respond is a consequence
of what my brain did to produce the output.
Lets think for a moment about physical events such as
the status of bytes in the computer. Each bit in each sequence
of 8 bits is either on or off. You can physically describe
the nature of this machine and the hardware, but you can also
think about representation: What does that pattern of physical
activity stand for, and represent in its absence? You can
think about interpreted rule-based systems, where the representations
have an impact on other parts of a system, causing other representations
to be formed, or combined, or operated on in various ways,
and outputs to be generated. In this regard it's useful to
think about computation in a computer to describe how the
mind works, even though its a wrong metaphor for the
A computer is based on a Von Neumann architecture, where you
have a strict separation between memory and the central processing
unit. This means that there is a strict separation between
operations and representations, which sit passively in memory.
The central processing unit is essentially a switching device
that uses instructions to dictate what its going to
do, both in terms of how it interprets successive sets of
instructions and what it does with the representations. The
very idea of representation depends on how the CPU is set.
The exact same pattern of bytes can represent a number, a
letter, or part of a picture depending on how its being
interpreted. Once an operation is performed, the results go
back into memory, and serve as input for additional processes.
The computer is useful as a way of thinking about all of this,
but its not going to turn out to be a model of how the
brain works; the brain doesnt work like this at all.
The critical thing about the computer in thinking about
computation as a model for understanding the brain is that
it really lets us think about how advanced the mutual interaction
of different levels of analysis is. Its a wonderfulmystery.
How can an idea arise from wet stuff? How can an idea influence
whats going on with the wet stuff? Here, the analogy
really works with the computer. We really can think about
the notion of representation in the computer, and how it dictates
the sequence of physical events through the organization of
Even though I try to track developments in computational ideas,
I'm not known as somebody who espouses the computational model
of mind. Nor am I considered a neuroscientist. In fact, as
far as I can see, Im not known as a card-carrying member
of any particular approach or subfield. Ive always been
on the fringe. When I was a graduate student I stumbled onto
the basic phenomenon I've been studying for 30-odd years now.
In my first year of graduate school at Stanfordthis
was 1970the idea of semantic memory was really hot.
Collins and Quillian had published a simulation model in 1969
in which they claimed that information is stored in long-term
memory in the most efficient way possible. (This makes no
sense for the brain, by the way, since storage space is apparently
not an issue although it is in a computer.) They posited that
memories are organized into hierarchies in which you store
information in as general a representation as possible. For
example, for animals, you've got a representation of animals,
and then birds, mammals, reptiles, etc. And then under birds
you have canaries, robins, etc. The notion was that you store
properties as high up in the hierarchy as you can rather than
redundantly duplicating them. For example, birds eat, but
so do lizards and dogs, so we store this property higher,
up with the concept of animals. You tag the exceptions in
a lower level.
One way to test this theory was to look at response times.
If you give somebody a statement like "A canary can sing,"
that information should be stored right in the same place
and "canary" and "sing" should be bound
together. But if you ask him, "Can a canary eat?"
he should have to traverse the network to find a connection
between the two (assuming that "eat" is stored up
with "animal"). It should take a little longer,
and it does! Unfortunately for the model, distance in a semantic
net turned out not to be crucial. My first year project at
Stanford showed that the response time was just due to how
closely associated the terms were, not distance in a net.
One of the experiments was particularly interesting. In one
item I asked people to verify the statement: "A flea
can bitetrue or false?" Two people in a row said
false, and afterwards I asked them why. One said that he "looked
for" a mouth, and couldnt find one. The other said
he "looked for" teeth and couldnt "see"
any. This idea of "looking for" and "seeing"
didnt fit in at all with Collins and Quillian's network-based
computer model, so I started thinking about it. My idea was
that maybe imagery has something to do with this. I telephoned
everybody whom Id tested already, and asked them if
they had tended to visualize when they were answering the
question. Roughly half said they did and about half said they
didnt. I simply plotted the data separately for the
two groups. What was dictating the response time of the people
who said they didnt use imagery was how associated the
properties were with the objects. For the people who used
imagery, that had nothing to do itit was how big the
I immediately designed an experiment where I pitted the two
characteristics against each other. For example, I asked people,
"True or false?: A mouse has a back," which is a
trait that is big but not highly associated. I also asked
whether it has whiskers, which is small and highly associated,
or wings, which is not true. I found that if I instructed
people to visualize, the critical thing was how big the properties
were. The bigger they were the faster the responses were.
If I asked them not to visualize, but to answer intuitively
as fast as they could, the pattern reversed. In this case,
the response speed depended on how associated the traits were,
not how big they were.
The next question was how to think about these results. Fortuitously,
at the same time I was doing these experiments I was taking
a programming class. This was in the days when you used punch
cards. You had to go to the computer center, submit your stack
of cards, and stand around looking at a monitor, waiting for
your job to come up and see whether it bombed, which you could
tell by how long it was running. At the end they gave you
a big printout. One of the exercises in the class was to program
a set of little modules that generated geometric shapes, like
triangles and squares and circles, and to adjust how big they
were and where they were positioned. You had to do things
like make a Christmas tree by recursively calling the same
routine that generated a triangle, and plotting the triangle
at different sizes in different positions, overlapping them
to produce the design.
As I was doing this, it suddenly occurred to me that this
is an interesting model of mental imagery. We could think
of imagery as having four main components: Its got a
deep representation, which is an abstract representation in
long-term memory; its got a surface representation,
which is like a display in a cathode ray tube; its got
generative processes between the two , so the surface geometry
is reconstructed in the surface image on the basis of the
deep representation; and, finally, it's got interpretative
processes that run off the surface image, interpreting the
patterns as representing objects, parts or characteristics.
This metaphor was neat, and led me to conduct a lot of fruitful
research. But it had the drawback that no matter how hard
you hit somebody in the head, youre not going to hear
the sound of breaking glassthere's no screen in there.
Even if there were, would just be be back to that problem
of the little man looking at the screen. This immediately
led me to start thinking about how to program a system where
there are arrays that function as a surface image and points
that are positioned in space depicting the pattern that represents
an object. And then you have something much more abstract
that's operated on to produce that.
One of the real virtues of thinking by analogy to the computer
is that it focuses you on the idea of processing systems
not just isolated representations or processes, but sets of
them working together. Nobody had ever tried to work out in
detail what a processing system that uses images would look
like. In fact, the few detailed models of imagery that existed
all focused on very specific, artificial tasks and tried to
model them using standard list-structures there were no
images in the models of imagery. We decided to take seriously
the idea that perhaps mental images aren't represented the
same way as language; perhaps they really are images. Steve
Schwartz and I built a series of simulation models that showed
such an approach is not only possible, but allows you to account
for much data. We published our first paper on this in 77,
and another in 78. I also wrote a book on it in 1980
called Image in the Mind, where I worked this out in much
more detail than anyone ever cared about. As far as I can
tell it had almost no impact. I remember asking one my professors
at Stanford about it, and he thought the book was too detailed,
and that for somebody to start working on the topic now theyd
have to look at it, think about it and get into it, and it
was just too much trouble. Psychologists generally don't like
having to work with a really detailed theoretical framework,
and that was basically the end of it. I have a mild frontal
lobe disorder that leads me to perseverate, and thus I've
continued to work out the theory and do experiments anyway.
My 1994 book on imagery is a direct outgrowth of the earlier
work, but now maps it into the brain. The Europeans (especially
the French) and Japanese seem interested, if not the Americans.
That said, I should note that lately there are signs that
interest in mental imagery might be picking up again. This
might be a result of another round in my old debate with Zenon
Pylyshyn. Hes a good friend of Jerry Fodor, but unlike
Fodor, Pylyshyn has maintained forever that the experience
of mental images is like heat thrown off by a light bulb when
youre reading: It's epiphenomenal, it plays no functional
role in the process. Pylyshyn believes that mental images
are just language-like representations and that its
an illusion that theres something different about them.
He published his first paper in 1973. Jim Pomerantz and I
replied to it in 1977 and the debate has just been rolling
along ever since. Pylyshyn has great distain for neuroscience,
to put it mildly. He thinks it's useless, and has no bearing
at all on the mind.
I really dont know what brings him to this conclusion.
I suspect its because he is one of the few (less than
2 percent of the population) people who does not experience
imagery. He apparently doesn't even "get" jokes
that depend on images. He also probably rejects the very idea
of imagery on the basis of of his intuitions about computation,
based on Von Neumann architecture. He's clearly aware that
computers dont need pictorial depictive representations.
His intuitions about the mind may be similar. But this is
Pylyshyn is not only against theories that are rooted in neural
mechanisms (he thinks theories of the logical structure of
language should be a model for all other types of theories
really!), he's also against neural network computational models.
I've probably published eight to ten papers using network
models. At one point in my career I did work on the nature
of spatial relations. I had the idea that there are actually
two ways to represent relations among objects. One is what
I call categorical, where a category defines an equivalence
class. Some examples of this would be "left of,"
"right of," "above," "below,"
"inside," "outside," etc. If you are sitting
across from me, from your point of view, this fist is to the
right of this open palm, and that is true for all these different
positions [moving his hand about, always to the right of the
vertical axis created by his fist]. "Right of" defines
a category, and even though I move my hand around, all of
these positions are treated as equivalent.
This is useful for doing something like recognizing a hand,
since the categorical spatial relations among my fingers,
palm, digits, and joints do not change. That's handy for recognizing
objects because if you store a literal picture in memory,
an open-palm gesture might match well, but if I make another
gesture with my hand, say clenching it, this would not match
well so you want something more abstract.
Categorical spatial relations are useful computationally for
that problem, but theyre not useful at all for reaching
or navigating. Just knowing that a fist is to the left of
this palm wont allow me to touch it precisely; Ive
got to know its exact location in space. If Im walking
around the room knowing the tables in front of me, "in
front of" is a categorical relation and thus is true
for an infinite number of positions relative to it. This is
not good enough for navigating. Thus, I posit a second type
of spatial relation, which I call coordinate: Relative to
some origin, the metric distance and direction is specified.
In my lab we have shown that the left cerebral hemisphere
is better at encoding categorical spatial relations, which
makes sense because categories are often language-based. On
the other hand (hemisphere), the right hemisphere is better
at encoding coordinate spatial relations. This is true in
normal people, it's true when we and others have done neuroimaging,
and it's true when you look at deficit sin patients who have
brain lesions. We've constructed a whole raft of neural network
models that showed that, in fact, if you split a modela
networkinto two separate streams (one for each type
of representation), it does better than if you have a single
system trying to make both categorical and coordinate representations.
The point is not so much that the hemispheres are different,
but rather that the brain relies on two distinct ways to code
spatial relations. This claim caused a mini-controversy. I'm
delighted to see that in a recent issue of the Journal of
Cognitive Neuroscience researchers (who I don't know) tested
over a hundred people after they "turned off" one
hemisphere at a time for medical reasons, and showed that
with challenging tasks where you have to make categorical
versus coordinate spatial relations judgments, the laterality
effects I predicted worked beautifully. If it was too easy
it didn't work, which also fits perfectly with our modeling
and previous experiments so it looks like this controversy
has been settled (but experience has taught me that those
are famous last words .).
This is really just one little corner of what I do, and ultimately
is related to my imagery work. Ive always argued that
imagery has to be understood in a system that includes language-like
propositional representations as well as depictive representations.
I dont think of the mind as purely imaginal. That can't
be true. It's got to depend on coordinating many different
types of representations that interact in intricate and interesting
ways. The distinction between the two types of representations
invites a further distinction between different forms of imagery,
which make use of the different sorts of spatial relations.
And in fact we have evidence for such a distinction. One clear
conclusion from all this: "Imagery" isn't just "one
Getting back to computational theorizing in psychology per
se. From my perspectiveand maybe Im missing somethingcomputational
theorizing has reached a plateau. Thats not to say there
isnt progress, but its incremental and is currently
within a paradigm that was set perhaps ten years ago. I dont
see any revolutionary work out there. Right now, the connectionists
are probably the leaders in computational theorizing relevant
to the brain. David Rummelhart did terrific work. Terry Sejnowski
is excellent, as is Jay McClelland. These are people who have
been at it for years. I don't see too much really new on the
In terms of interesting theorizing, Dan Dennett and Steve
Pinker and their colleagues are trying to cash out the evolutionary
psychology program. Instead of trying to think about behaviors
as being the products of evolution, they are thinking about
how the modular structure of information processing in the
brain is a consequence of evolution. That's an interesting
program. My objection is that this enterprise is not particularly
empirical. Science is the process of finding things out. You've
got to go out and do studies to find things out. It's very
helpful to have theories as a base from which you can direct
your attention to issues and questions, but then youre
got to go do the actual research.
If you asked me to explain the direction of mind science writ
large, I'd say that what youre going to see is a bridging
between cognitive neurosciencewhere the mind is conceived
of as what the brain doesand genetics. Those are the
two really hot areas right now, and theres a giant gulf
I was recently writing a introductory psychology textbook
chapter on intelligence, and read a lot of behavioral genetics.
I was really struck by the fact that these guys are trying
to bridge the gap from genes to behavior in one fell swoop,
and theyre not doing that good a job at it. They're
not doing that well in linkage studies that try to connect
variability in a behavior with variability in different types
of alleles. Sometimes they manage 2% of the variance. It occurred
to me that theyre leaving out the middle man. They want
to think in terms of the model: genes > behavior.
But it would be much better to think in terms of: genes >
brain, and then brain > behavior. Genes influence
behavior and cognition via what they do to the brain. Thinking
about this has gotten me very interested in genetics, but
not in the sense that genetics is a blueprint. Most genes
functioning in the adult brain seem to be up-regulated and
down-regulated by circumstances. They turn on and off.
Heres an example developed by Steve Hyman that can serve
as a metaphor: If you want to build muscles you lift weights.
If the weight is heavy enough its going to damage the
muscles. That damage creates a chemical cascade and reaches
into the nuclei of your muscle cells, and turns on genes that
make proteins and build up muscle fibers. Those genes are
only turned on in response to some environmental challenge.
Thats why youve got to keep lifting heavier and
heavier weights. The phrase, "No pain no gain,"
is literally true in this case. Interaction with the environment
turns on certain genes which otherwise wouldnt be turned
on; in fact, they will be turned off if certain challenges
arent being faced. The same is true in the brain. Growing
new dendritic spines, or even replenishing neurotransmitters,
is linked to genes that are being turned on and turned off
in response to what the brain is doing, which in turn is responding
to environmental challenges.
I'm really interested in how genes allow the brain to respond
to the tasks at hand. When genes are turned on and off, this
affects what neurons are doing; which then, of course, affects
how blood is allocated; in turn, affecting cognition and behavior.
There is a gigantic project yet to be done that will have
the effect of rooting psychology in the rest of natural science.
Once this is accomplished, you'll be able to go from phenomenologythings
like mental imageryto information processingthinking
about things you can model on the computerto the brainthinking
about how a particular kind of information processing arises
from this particular brain we havedown through the workings
of the neurons, including the biochemistry, all the way to
the biophysics and the way that genes are up-regulated and
This is going to happen; I have no doubt at all. When it does
were going to have a much better understanding of human
nature than is otherwise going to be possible. If you want
to understand evolution, the residue of evolution is the genes.
Why not study the genes if you want to understand the reasons
behind the brain's organization? There are reasons we have
those genes rather than other onesthats where
the evolutionary story comes in. But my particular brain or
your particular brain is the way it is not only because of
the particular genes we have, but also because of the way
the environment up-regulated or down-regulated those genes
during development, sculpting our brains certain ways, and
the ways our genes respond to environmental and endogenous
challenges. All of this is empirically tractable. The tools
are available, the questions are clear, and we know what sort
of answers to seek. Time to get cracking!