How Molecules Matter to Mental
Computation
Paul Thagard
Philosophy Department
University of Waterloo
Thagard, P. (2002). How molecules matter
to mental computation. Philosophy of Science, 69, 429-446.
Abstract
Almost all computational models
of the mind and brain ignore details about neurotransmitters,
hormones, and other molecules. The neglect of neurochemistry in
cognitive science would be appropriate if the computational properties
of brains relevant to explaining mental functioning were in fact
electrical rather than chemical. But there is considerable evidence
that chemical complexity really does matter to brain computation,
including the role of proteins in intracellular computation, the
operations of synapses and neurotransmitters, and the effects
of neuromodulators such as hormones. Neurochemical computation
has implications for understanding emotions, cognition, and artificial
intelligence.
1. Introduction
The functioning of brains in humans
and other animals involves dozens of chemical messengers, including
neurotransmitters, hormones, and other molecules. Yet almost all
computational models of the mind and brain ignore molecular details.
Symbolic models such those based on production rules abstract
entirely from neurological details (e.g. Anderson 1993; Newell
1990). Neural-network computational models typically treat neuronal
processing as an electrical phenomenon in which the firing of
one neuron affects the firing of all neurons connected to it by
excitatory and inhibitory links (e.g. Churchland and Sejnowski
1992; Eliasmith and Anderson forthcoming; Levine 2000; Parks,
Levine, and Long 1998; Rumelhart and McClelland 1986; see also
such journals as Cognitive Science, Neural Computing, Neural
Networks, and Neurocomputing). The role of neurotransmitters
and other molecules in determining this electrical activity is
rarely discussed.
The neglect of neurochemistry in cognitive science would be appropriate
if the computational properties of brains relevant to explaining
mental functioning were in fact electrical rather than chemical.
But there is considerable evidence that chemical complexity really
does matter to brain computation. I will review that evidence
by discussing the role of proteins in intracellular computation,
the operations of synapses and neurotransmitters, and the effects
of neuromodulators such as hormones. Attending to the ways in
which the brain is a chemical as well as an electrical computer
provides a qualitatively different view of mental computation
than is found in traditional symbolic and connectionist models.
I conclude with a discussion of the implications of neurochemical
computation for issues involving emotions, cognition, and artificial
intelligence. First some general remarks are needed concerning
the explanatory functions of computational models of mind.
2. Modeling the Mind
During the 1930s, Alan Turing and others
produced rigorous mathematical accounts of computation, and in
the 1940s the first general digital computers were built. The
development of the theory and practice of computation had a huge
impact on psychology and the philosophy of mind, because it showed
how thought could plausibly be construed as mechanical. Psychologists
such as George Miller and philosophers such as Hilary Putnam recognized
the computational construal of mind as a powerful alternative
to behaviorist ideas that had tried to make the mind go away.
Allan Newell and Herbert Simon and other researchers began to
produce computer programs that model intelligent behavior.
Abstract models of computation include the Turing machine, which
is an imaginary device consisting of a tape with squares that
contain a 0 or 1 and a head that can move from square to square.
A table of very simple instructions completely determines the
movements and reading and writing behavior of the head. The Turing
machine, and mathematically equivalent abstractions such as recursive
function theory, are very useful for clarifying what computation
is. But they play no direct role in explaining particular mental
functions. In order to explain particular kinds of mental abilities
such as problem solving and language use, researchers develop
specific kinds of computational models that posit mental representations
such as rules and computational procedures such as forward chaining
that operate on those rules. Rule-based systems are much better
cognitive models than Turing machines because they concretely
describe mechanisms that can replicate mental behavior. As far
as abstract computational power goes, rule-based systems are no
more powerful than a Turing machine, but they are much closer
to capturing the mechanisms that underlie cognitive functions.
Besides rules, many cognitive scientists espouse alternative or
complementary ways of modeling the mind, involving such representations
as concepts, mental models, analogies, visual imagery, and artificial
neural networks (see Thagard 1996 for a concise survey). In particular,
artificial neural networks have the same abstract computational
power as Turing machines and rule-based systems, but they are
advocated by many researchers because they implement structures
and procedures that seem to capture more closely the operations
of the brain. For example, the brain uses distributed representations
in which symbolic information is represented collectively by numerous
simple neuronal elements, and uses massively parallel computations
to draw inferences. Neural networks can be used to implement rule-based
systems, but they also can support modes of computing qualitatively
different from those in rule-based systems.
Most cognitive models using artificial neural networks describe
the behavior of neurons by a parameter called activation, which
represents the firing rate of the neuron, i.e. the rate at which
it sends an electrical signal to other neurons. Recent models
have more sophisticated dynamics, describing not only the rate
of firing but the pattern of firing. Consider, for example, a
neuron that fires 5 times, with the firing state represented by
a 1 and the resting state represented by a 0. The firing pattern
10101 and the pattern 11100 both show the same rate of activation
(firing 3 times out of 5), but they can represent very different
neuronal behaviors. Neural networks that take into account such
firing patterns are called spiking or pulse networks,
and they have computational advantages over networks that only
use rate codes. For example, there are functions that can be computed
by a single spiking neuron whose computation would require many
traditional rate-coding neurons (Maass and Bishop 1999, p. 79).
Moreover, spiking neurons have psychologically important qualitative
properties such as becoming synchronized with each other, and
neural synchrony has been proposed as a crucial ingredient in
inference, analogy, and consciousness (Engel et al. 1999, Hummel
and Holyoak 1998, Shastri and Ajjanagadde 1993). Thus spiking
neural networks provide a promising new approach to computational
modeling of the brain.
I have gone into this brief review of cognitive modeling to indicate
the form of argument that I want to develop. Just as rule-based
models capture aspects of cognition that Turing machines do not
address, and just as neural networks capture aspects of cognition
that rule-based systems do not address, and just as spiking neural
networks capture aspects of cognition that rate-coded neural networks
do not address; so chemical neural networks have the potential
to illuminate aspects of thinking that purely electrical neural
networks do not adequately address. In order to provide a useful
supplement to existing computational accounts of mind, a new account
must show that it has quantitative and qualitative advantages
over old models, suggesting mechanisms of mental computing that
are more powerful and more biologically and psychologically natural
than in previous models. My task is to show that such advantages
are to be found in chemical neural networks that explicitly recognize
molecular mechanisms.
I do not mean to suggest that molecular models should supersede
existing ones. Models are like maps, intended to correspond to
reality but varying greatly in the level of detail that is useful
for different purposes. To determine that Italy is south of Switzerland,
a large scale map of the world is appropriate, whereas a much
more detailed map is better for hiking in the Alps. Similarly,
there are aspects of mental computing that are conveniently and
accurately describable by rule-based systems and traditional electrical
neural networks, but there also aspects for which it is explanatorily
useful to move down to the molecular level.
3. Proteins and Cells
How neurons and neural networks can
perform computations is well understood. Each neuron receives
and integrates electrical signals from other neurons, then passes
its own signal on to other neurons to excite or inhibit their
signaling. Neural networks are Turing complete, in that they can
compute any function that a Turing machine can, and more importantly
they can behave in ways that account for human cognitive functions.
Only recently, however, have the computational capabilities of
non-neuronal cells been appreciated.
The human body contains trillions of cells, and a typical cell
contains around a billion protein molecules, with about 10,000
different kinds of protein in each cell. (Lodish et al., 2000).
The outer membranes of cells have receptors, which are
proteins that bind signaling molecules circulating outside the
cells. The receipt of a signaling molecule by a receptor activates
signal-transduction proteins within the cell that initiate
chemical reactions affected by enzymes, which are proteins
that accelerate reactions involving small molecules. The chemical
pathways within a cell can lead to diverse results, including
cell division producing new cells, cell death, and the production
of new signaling molecules that are expelled from the cell and
then circulate to be bound by the receptors of other cells. For
example, when the hormone epinephrine (also known as adrenaline)
is produced by the adrenal gland in response to fright or heavy
exercise, it circulates through the blood stream and binds to
cells with appropriate receptors. These include liver cells that
are stimulated to emit glucose into the blood stream, and heart
muscles cells that increase the heart's contraction rate and the
supply of blood to the tissues. The result is an increase in available
energy for major motor muscles.
We can think of individual cells, whether neurons or not, as computers
that have inputs in the form of molecules that bind to receptor
proteins, outputs in the form of molecules emitted from the cells,
and internal processes carried out by chemical reactions involving
proteins (Gross, 1998). Proteins can function as on-off switches,
for example by the process of phosphorylation in which
proteins are modified by adding groups of atoms including phosphorus.
Signals within a cell can be rapidly amplified by enzymes that
can each activate hundreds of molecules in the next stage of processing.
Molecular computing within the cell is massively parallel, in
that many receptors can simultaneously initiate many chemical
reactions that proceed concurrently in the billion or so proteins
in the cell.
Multi-cellular computing also exhibits massive parallelism as
cells independently receive and send signals to each other. There
are three types of signaling by secreted molecules (Lodish et
al., 2000, ch. 20). In autocrine signaling, a cell signals
itself by secreting molecules that bind to its own receptors.
For example, cells often secrete growth factors that stimulate
their own proliferation. In paracrine signaling, a secretory
cell signals an adjacent cell that has receptors for the secreted
molecules. Neuronal signaling is paracrine, with neurotransmitters
as the molecular signals, but there are also other kinds of paracrine
signaling involved in cellular communication. Adjacent cells can
also communicate with each other more directly than via secretions,
by means of the attachment proteins that enable cells to adhere
to each other and form tissues. The third type of signaling by
secreted molecules is endocrine, in which a cell secretes
a molecule, called a hormone, that travels through blood
vessels to be received by distant target cells that may be several
meters away. The computational functions of hormones are discussed
in Section 5.
Is describing proteins and cells as performing computations a
stretched metaphor that violates the mathematically precise notion
of computation developed by Turing and others? Not at all, for
there are several recent mathematical and experimental results
that show that molecular processing is computational in the most
rigorous sense. Magnasco (1997) proved that chemical kinetics
is Turing universal in that the operations of a Turing machine
can be carried out by chemical reactions. Bray (1995) showed how
protein molecules can function as computational elements in living
cells and can even be trained like a neural network. Adleman (1994)
demonstrated that a hard combinatorial problem in computer science
could be solved by molecular computation involving strands of
DNA. DNA can provide cells with a kind of permanent memory, whereas
protein operations serve to process information. Thus the description
of cells and proteins as carrying out computations is more than
metaphorical, and therefore is potentially relevant to understanding
mental computation. Whether it is actually relevant requires looking
more closely at the behavior of neurons.
4. Neurotransmitters
4.1. Properties of Neurotransmitters
The last section discussed the signaling
capabilities of cells in general, but was not meant to suggest
that organs such as the liver have mental properties. Human minds
depend on a particular kind of organ, the brain, which has billions
of cells capable of interacting with each other in special ways.
A typical neuron takes input from more than a thousand neurons,
and provides output to thousands of others, via special connections
called synapses. Some synapses are electrical, passing ions directly
from one cell to another, but most are chemical, enabling neurons
to excite or inhibit each other by means of neurotransmitters
that pass from the presynaptic cell to the postsynaptic cell.
Neurotransmitters are not the only chemicals that allow one neuron
to influence another; the next section will discuss hormones and
other molecules that modulate the effects of neurotransmitters.
Human brain chemistry is fundamentally the same as that found
in other vertebrates.
The most important neurotransmitters include: aspartic acid and
glutamic acid, (excitatory), gamma-aminobutyric acid and glycine
(inhibitory), epinephrine (also a hormone), acetylcholine, dopamine,
norepinephrine, serotonin, histamine, neurotensin, and endorphins.
Does the abundance of different neurotransmitters used by the
brain matter to mental computation? One might argue that the only
computational significance is in the excitatory and inhibitory
behavior of synaptic connections, and that the particular chemicals
involved in excitation and inhibition are largely irrelevant to
how the brain computes. I propose, however, that the array of
neurotransmitters makes both qualitative and quantitative differences
to mental processing, affecting both its style and speed.
The computational operation of a neural network depends on three
kinds of properties of the network. The first is the internal
processing capability of the neurons in the network, which may
vary depending on how much the neuron can do with the various
inputs coming to it and how complex its outputs can be. Most models
of artificial neural networks used in cognitive science have very
simple processing power, enabling them to translate input activation
into output activation. Spiking neural networks have greater internal
processing power in that they can respond differently to different
patterns of spikes coming into them, and they produce different
output patterns of spiking behavior, not just a rate of activation.
The discussion of the computational power of proteins in Section
3 showed that chemical neurons have still greater internal processing
power than those found in artificial spiking networks, because
the chemical reactions that occur within cells are qualitatively
and quantitatively different from the electrical integration and
firing performed by spiking neurons.
The second key property is the topography of the network, which
is the pattern of connectivity that enables one neuron to affect
the firing of another. In typical artificial neural networks,
topography is determined by the excitatory and inhibitory links
that connect neurons, but we shall see that chemical brains have
a greatly enhanced topography. The third key kind of property
is temporal. A neural network is a dynamic system that evolves
over time, and how it evolves is very much affected by the order
and rate of different occurrences in it. For example, artificial
neural networks are sometimes synchronous, with all neurons having
their activations updated at the same time, but it is more biologically
natural when they are asynchronous. Real neurons are asynchronous
and depend on temporal history in the form of the spike patterns
that are input to them. Spiking neural networks thus have temporal
properties that are different from rate-activation networks, although
they are no different topographically from rate-activation networks.
Chemical networks differ in all of these kinds of properties
internal processing, topographical, and temporal - from purely
electrical networks. I will now discuss the topographic and temporal
effects of neurotransmitters and neuromodulators.
4.2. Topographic Effects of Neurotransmitter Pathways
Neutrotransmitters occur in specific
nerve pathways in the brain (Brown 1994, p. 70). A pathway consists
of connected neurons whose synapses all involve the transmission
of the same chemical. For example, there are specific pathways
for acetylcholine, dopamine, norepinephrine, and serotonin. Different
pathways have different functions, for example the integration
of movement by dopamine and the regulation of emotion by serotonin.
Disruptions in these pathways can cause various mental illnesses,
for example Parkinson's disease resulting from lack of dopamine,
and depression resulting from lack of serotonin. Drugs can be
used to treat illnesses by increasing or decreasing the amounts
of neurotransmitters, as when MAO inhibitors are used to treat
depression by increasing the availability of dopamine and serotonin.
The computational significance of neurotransmitter pathways is
that they provide the brain with a kind of organization that is
useful for accomplishing different functions. If a neuron could
be connected to any other neuron, it would be difficult to orchestrate
particular patterns of neuronal behavior. The brain requires cascades
of activity, for example when perception of a dangerous object
such as a snake leads to activation of fear centers in the amygdala
and release of stress hormones. Neurotransmitters provide a course
kind of wiring diagram, organizing general connections between
areas of the brain that need to work together to produce appropriate
reactions to different situations. Of course, the brain might
have evolved with purely electrical pathways, but the fact is
that the different kinds of neurotransmitters have served to establish
patterns of connectivity that are important for its operation.
Neurotransmitters serve to restrict connectivity within the brain,
but different kinds of chemical communication that enhance connectivity
are discussed in Section 5.
4.3. Temporal Effects of Neurotransmitters
There are two types of synapse, the
relatively rare electric synapse and the more common chemical
synapse in which neurotransmitters are emitted from the vesicles
of the presynaptic cell and bind to the receptors of the postsynaptic
cell. The effects of chemical synapses are electrical, allowing
ions to cross the membrane of the postsynaptic cell. But these
effects are much slower than in an electric synapse, in which
ions move directly from one to neuron to another (Lodish et al.,
2000, p. 943). Heart cells, for example, are electrically coupled,
allowing groups of muscles cells to contract in synchrony. Signals
are transmitted across electric synapses in a few microseconds,
without the delay of .5 milliseconds found in chemical synapses.
Given the greater speed and reliability of electric synapses,
it might seem puzzling why most synapses are chemical. According
to Lodish et al. (2000, p. 942), chemical synapses have two important
transmission advantages over electric ones. The first is signal
amplification, for example when a single presynaptic neuron
causes contraction of multiple muscle cells. The second is signal
computation, in which a single neuron is affected by signals
received at multiple excitatory and inhibitory synapses. "Each
neuron is a tiny computer that averages all the receptor activations
and electric disturbances on its membrane and makes a decision
whether to trigger an action potential." (Lodish p. 943).
Thus chemical synapses, even though slower, allow for more flexible
kinds of computation.
In chemical synapses, there are two classes of neurotransmitter
that operate at vastly different speeds (Lodish, et al., 2000,
939). Fast synapses, using receptors to which neurotransmitters
bind and cause an immediate opening of ion channels, enable ions
to cross the postsynaptic cell membrane in less than 2 milliseconds.
In contrast, slow synapses are more indirect, requiring binding
of a neurotransmitter to a receptor that initiates a chemical
reaction that eventually affects ion conductance. Such postsynaptic
responses are slower and longer lasting than those involving fast
synapses, working on a scale of seconds rather than milliseconds.
Particular neurotransmitters can have special temporal properties.
One kind of glutamate receptor, the NMDA receptor, functions as
a coincidence detector (Lodish et al. 947). These receptors only
open a channel if two conditions are met: glutamate must be bound
and the membrane must be partly polarized by previous transmission.
Thus the NMDA receptor makes possible a simple kind of learning.
Galarreta and Hestrin (2001) found that networks of neurons that
release gamma-aminobutyric acid (GABA) spike fast enough to be
able to detect synchrony in input neurons. It used to be thought
that each neuron could only release one kind of neurotransmitter,
but there is evidence that a neuron can release different transmitters
and different amounts and combinations of transmitters at different
times (Black 1991, p. 79). This complexity makes possible a degree
of electrochemical encoding that has more variables than the activations
and spike trains in purely electrical networks.
In sum, the different temporal properties of neurotransmitters
enable them to operate on very different time scales, ranging
from microseconds (electric synapses) to milliseconds (fast chemical
synapses) to seconds (slow chemical synapses). We will see in
the next section that even longer time effects are possible with
hormones.
5. Neuromodulators
Brown (1994, p. 14) provides a useful
taxonomy of neuroregulators, the chemicals that affect
neuronal activity, dividing them into neurotransmitters and neuromodulators.
As just described, neurotransmitters are released by neurons and
act on other neurons via synapses. Neuromodulators, in contrast,
can be released by non-neuronal cells as well as neuronal cells,
and they act non-synaptically on both the presynaptic and postsynaptic
cell to alter synthesis, storage, release, and uptake of neurotransmitters.
Neuromodulators include hormones, which travel through the bloodstream,
and non-hormone molecules that pass more directly between cells.
The point of this section is to argue that the variety of neuromodulators
used by the brain expands its computational abilities in ways
that help to explain aspects of human thinking. Contrary to most
computational models of neural network, whether a neuron fires
is not simply a function of its synaptic input. The influence
of neuromodulators affects both the topographical and temporal
properties of neural networks.
5.1. Topographical Effects of Neuromodulators
Neuromodulators dramatically change
the causal structure of a neural network. Instead of having a
kind of local causality, in which whether a neuron fires is determined
only by the neurons that provide synaptic inputs to it, it becomes
possible for neurons and other cells that are even meters away
to affect firing. A neuron in one part of the brain such as the
hypothalamus may fire and release a hormone that travels to a
part of the body such as the adrenal glands, which stimulates
the release of other hormones that then travel back to the brain
and influence the firing of different neurons. Complex feedback
loops can result, involving interactions between the neurotransmitter
control of hormone release and the hormonal regulation of neurotransmitter
release. These feedback loops can also involve the immune system,
because brain cells also have receptors for cytokines, which are
protein messengers produced by immune system cells such as macrophages.
How do hormones affect neuronal firing? The internal processing
of a neuron depends on a host of inputs, including neurotransmitters,
hormones, and growth factors (Brown, 1994, p. 200). All of these
are first messengers that activate proteins to produce
intracellular signals via second messengers such as the molecule
cAMP, which then activate specific protein kinases (enzymes) that
function as third messengers. The kinases phosphorylate proteins
that act as fourth messengers to stimulate changes in membrane
permeability and protein synthesis in the cell. Such changes influence
the ability of the neuron to spike, and hence affect the rate
and pattern with which it fires. The key point here is that whether
a neuron fires and hence contributes to the computation performed
by the neural network is not simply a function of neurons that
provide synaptic inputs, but can also be affected by a host of
other cells that produce hormones. Hence the topography of the
brain is far more complex than recognized by purely electrical
models in which the inputs to artificial neurons are just activations
and spike trains.
Hormonal chemical effects operate over long distances, but there
are also non-synaptic connections between adjacent neurons. Cell
adhesion molecules not only bind cells together to form tissues,
they also carry signals between cells that can affect their development
(Crossin and Krushel, 2000). Song et al (1999) discovered Neuroligin,
a synaptic cell adhesion molecule that not only enables neurons
to establish synaptic connections with each others, but also allows
for direct signaling from the postsynaptic neuron back to the
presynaptic one. Such retrograde signaling is thought to be important
for learning. Other molecular mechanisms for retrograde signaling
have been identified. The postsynactic neuron can also send chemical
signals back to the presynaptic neuron by means of gases such
as nitric oxide and carbon monoxide, or by peptide hormones (Lodish
et al., 2000, p. 915). Nitric oxide is a small molecule that can
easily diffuse to affect many neurons, greatly expanding the computational
topography of neural networks beyond synaptic connections. Koch
(1999, p. 462) conjectures that, because of the spread of nitric
oxide: "the unit of synaptic plasticity might not be individual
synapses, as assumed by neural network learning algorithms, but
groups of adjacent synapses, making for a more robust, albeit
less specific learning rule."
Neuronal firing is also affected by glial cells, which were formally
thought to function only to hold neurons together. There are 10-50
times more glial cells in the brain than neurons, and glial cells
affect both the formation of connections by nerve cells and their
firing. A factor released by glial cells makes transmitting neurons
release their chemical messengers more readily in response to
an electrical signal (Pfrieger and Barres, 1997). Stimulated glial
cells release calcium that trigger surrounding glia to release
calcium too, producing a spreading signal (Newman and Zahs 1998).
The calcium wave releases glutamate from the glial cells, which
has a direct impact on the firing of the neurons in the vicinity.
In sum, there is evidence from the behavior of hormones, nitric
oxide, and glial cells that the topography of brain networks is
far more complex than is captured by electrical models based only
on synaptic connections. Not surprisingly, the operation of non-synaptic
chemical messengers also affect the temporal patterns of neurons.
5.2. Temporal Effects of Neuromodulators
Hormones can affect the firing rate
of neurons (Brown 1994, p. 166f.). Gonadal hormones increase the
electrical activity of some neurons and inhibit the activity of
other neurons. For example, estrogen can modulate the release
of dopamine and serotonin. Thus hormones can slow down or speed
up neuronal firing.
Many neurons secrete neuropeptides such as endorphins and oxytocin.
Unlike classical neurotransmitters, these molecules are released
outside the synaptic zone, and can have effects that last for
hours or days (Lodish et. al, 2000, 936). Thus the temporal effects
of neuropeptides operate on a very different scale from the much
briefer effects of neurotransmitter emission described in Section
4.2.
Thus a computational system that involves neuromodulators can
be expected to have different temporal behaviors than one with
neurotransmitters only, and we already saw in Section 4.2 that
different neurotransmitters give rise to different temporal properties.
Hence molecules matter for the temporal behavior of neural networks.
6. Emotional Cognition
My general argument to this point has
been that there are reasons to expect that neurochemistry should
matter to mental computation, but I have not shown any particular
kinds of mental computation that are affected. There is little
direct evidence that the highest-level mental computations involved
in problem solving are tied to the influences of specific neurotransmitters
and neuromodulators. However, there is substantial evidence that
these neuroregulators are important for emotions, and there is
also evidence that emotions greatly affect problem solving and
learning. I will review these two bodies of evidence and conclude
that even the most cognitive of mental functions are subject to
neurochemical understanding. Chemistry has both positive and negative
effects on emotions and problem solving.
6.1. Emotions and Neurochemistry
Panksepp (1993) provides a concise
review of the neurochemical control of moods and emotions, including
examples of how neurotransmitters are linked to particular emotions.
Adminstration of glutamate, the most common excitatory neurotransmitter
in the brain, can precipitate aggressive rage and fear responses.
NMDA receptor blockage in the amygdala can modulate extinction
of fear behaviors. The inhibitory neurotransmitter GABA figures
in the control of anxiety. Norepinephrine influences sensory arousal
and becomes prominent in high-affect situations such as threat.
Dopamine is associated with positive emotionality, and adenosine
is a natural soporific that is blocked by weak mood enhancers
such as caffeine.
Neuroregulators also play prominent roles in specific emotions.
Corticotropin-releasing factor instigates a stress response that
has a major impact on fear and anxiety. Oxytocin enhances maternal
behavior as well as feelings of acceptance and social bonding,
and contributes to sexual gratification. Arginine vasopressin
is under testosterone control and can provoke male aggression.
Estrogen receptors in the brain are involved in female sexual
behavior, aggression and emotionality (Brown 1994, p. 154). Many
other peptides also affect emotional behavior.
Additional evidence concerning neurochemical influences on mood
and emotion comes from the medical effectiveness of drugs that
target particular neurotransmitters (Panksepp 1998, p. 117). Depression
can be treated both by drugs like Prozac that prolong the synaptic
availability of neurotransmitters such as serotonin and dopamine
and by drugs that inhibit the enzyme monoamine oxidase (MAO) that
normally helps degrade neurotransmitters following release. Antipsychotic
drugs used to treat schizophrenia generally dampen dopamine activity.
Most antianxiety agents interact with a specific receptor that
can facilitate GABA activity, whereas newer drugs reduce anxiety
by interacting with serotonin receptors. A new generation of psychiatric
medicines is being developed to deal with problems such as bulimia
that may arise from imbalances in particular neuropeptides.
There is thus abundant reason to believe that understanding of
human emotions will require attention to the effects of neuroregulators
on thinking. It follows immediately that neurochemistry is relevant
to understanding the nature of emotional consciousness. Feelings
of happiness, sadness, fear, anger, disgust and so on emerge from
brain activity by mechanisms not yet understood, but the diverse
ways in which neurochemicals influence emotion suggest that it
is unlikely that emotional consciousness emerges only from the
electrical activities of the brain. I return to this topic in
the discussion of artificial intelligence in Section 7.
6.2. Cognition
It might be argued that, even though
chemical explanations are relevant to emotion, they have no bearing
on central cognitive processes such as problem solving, learning,
and decision making. However, there is increasing evidence in
psychology and neuroscience that cognition and emotion are not
separate systems and that emotion is an intrinsic part of human
cognition (Dalgleish and Power, 1999). Reviewing this evidence
would take a book in itself, but here I will only report a few
salient examples of the cognitive impact of emotions.
Isen (1993) reviews an extensive literature on the impact of positive
affect on decision making. The presence of positive feelings can
cue positive materials in memory, making access to such thoughts
easier. Positive but not negative emotion provides retrieval cues
for situations relevant to a current problem. Positive affect
also promotes creativity in problem solving and negotiation, and
efficiency and thoroughness in decision making. People in whom
positive affect have been induced are able to categorize material
more flexibly and to see more similarities among items. Kunda
(1999, p. 248) reports that mood manipulations by small gifts
or pleasant music have been shown to influence a host of judgments,
including assessment of one's own competence, one's general satisfaction
of life, and evaluations of the quality of political leaders.
Affect may also influence our cognitive strategies: people in
a bad mood are more likely to use elaborate, systematic processing
strategies. Happiness has been found to increase our reliance
on social stereotypes, whereas sad people have reduced reliance
on negative stereotypes. Thus basic cognitive functions such as
categorization, problem solving and decision making are under
emotional influence.
Ashby, Isen, and Turken (1999) have developed a neuropsychological
theory of how positive affect influences cognition. They propose
that positive affect is associated with increased brain dopamine
levels that improve cognitive flexibility. Many readers of this
article are familiar with the enhancement in problem solving ability
brought about by caffeine, which blocks the inhibitory neurotransmitter
adenosine (Brown 1996). In contrast, alcohol can disrupt mental
functioning by inhibiting receptors for the excitatory neurotransmitter
glutamate, including NMDA receptors important for learning. (More
pleasantly, alcohol reduces anxiety by binding to GABA receptors
and increasing their inhibitory function, while inducing euphoria
through increased dopamine levels in the brain's reward centers
and released endorphins.)
It might be thought that decision making would improve if emotions
were removed from decisions, but the neurophysiological research
of Damasio (1994) and his colleagues suggests that this is emphatically
not the case. People who have brain damage that severs links between
the most cognitive areas of the brain in the neocortex and the
most emotionally important areas in the amygdala are very ineffective
decision makers, even though their verbal and mathematical abilities
are unaffected. Their problem is that they have lost the emotion-driven
ability to make decisions on the basis of what really matters
to them. Bechara et al. (1997) found that this disability also
made it difficult for patients to learn a card playing task in
which normal subjects unconsciously learned strategies that enabled
them to avoid bad outcomes.
This neurological research on the role of emotions in decision
making fits well with recent psychological theories that finds
deficiencies in purely cognitive accounts of decision. Loewenstein,
Weber, Hsee, and Welch (2001) show that many psychological phenomena
involving judgment and decision making under uncertainty can be
accounted for by understanding peoples estimates of risk as inherently
emotional. Similarly, Finucane et al. (2000) propose that human
decisions are heavily affected by what they call the "affect
heuristic". Legal and scientific thinking are also inherently
emotional (Thagard forthcoming-a, b).
I have mentioned only a small part of the evidence that challenges
the traditional psychological division between cognition and emotion
and the ancient philosophical distinction between reason and passion.
But it suffices for the purpose at hand, to show that the demonstrable
relevance of neurochemistry to emotions carries over to cognition
in general. If human cognition is mental computation, it is a
kind of computation determined by the chemical as well as the
electrical aspects of the brain. This conclusion has important
implications for the prospects of developing intelligence in non-human
computers.
7. Artificial Intelligence
Kurzweil (1999) and Moravec (1998)
have predicted that artificial intelligence will be able to match
human intelligence within a few decades. Their prediction is based
on the exponential increase in processing speed of computer chips,
which continues to double every 12-18 months as it has for decades.
Kurzweil estimates the computing speed of the human brain as around
20 million billion calculations per second, based on 100 billion
neurons each with a thousand connections and the slow firing rate
of 200 calculations per second. Assuming continued exponential
increase in chip speed, digital computers will reach the 20 million
billion calculations (on the magnitude of 10^15) per second mark
around 2020.
However, the molecular chemistry of the brain suggests that this
estimate of its computational power may be very misleading, both
quantitatively and qualitatively. If we count the number of processors
in the brain as not just the number of neurons in the brain, but
the number of proteins in the brain, we get a figure of
around a billion times 100 billion, or 10^17. Even if it is not
legitimate to count each protein as a processor all by itself,
it is still evident from the discussion in Section 3 that the
number of computational elements in the brain is more than the
10^11 or 10^12 neurons. Moreover, the discussion of hormones and
other neuroregulators discussed in Section 5 shows that the number
of computationally relevant causal connections is far greater
than the thousand or so synaptic connections per neuron. I do
not know how to estimate the number of neurons with hormonal receptors
that can be influenced by a single neuron that secretes hormones
or that activates glands which secrete hormones, but the number
must be huge. If it is a million, and if every brain protein is
viewed as a mini-processor, then the computational speed of the
brain is on the order of 10^23 calculations per second, far larger
than the 10^15 calculations per second that Kurzweil expects to
be available by 2020, although less than where he expects computers
to be by 2060. Thus quantitatively it appears that digital computers
are much farther away than Kurzweil and Moravec estimate from
reaching the raw computational power of the human brain.
Moreover, intelligence is not merely a matter of raw computational
power, but requires that the computer have a sufficiently powerful
program to produce the desired task. My Macintosh G4 laptop computer
can calculate 2^100,000 in a couple of seconds, the same amount
of time in which I can only calculate 2^5, but the computer lacks
the programming to be able to understand language and solve complex
problems. Kurzweil and Moravec are aware that it is a daunting
task to write the billions or trillions of lines of software that
would be needed to enable the superfast computers of the future
to approach human cognitive capabilities, but they blithely assume
that evolutionary algorithms will allow computers to develop their
own intelligent software. Evolutionary computation, which uses
algorithms modeled in part on human genetics, is indeed a powerful
means of developing new software (Koza 1992), but it is currently
limited by the need for humans to provide the evolving programs
with a criterion of fitness that the genetic algorithms serve
to maximize. In humans, the evaluation of different states is
provided by emotions, which direct us to what matters for our
learning and problem solving. Computers currently lack such intrinsic,
biologically-provided motivation, and so can be expected to have
difficulties directing their problem solving in non-routine directions.
Perhaps software will be developed that does for computers what
emotions do for us, but current computational research on emotions
is very limited compared to the complexity of the human emotional
system based on numerous neurotransmitters and neuromodulators.
There is a current resurgence in AI of interest in emotions, which
is however treated by researchers as a symbolic or electrical
rather than a chemical phenomenon. The complexity of human emotions,
based on looping interactions among neural, hormonal, and immune
systems, may be too complex for people to figure out how to program
and also too complex for a program created by humans to evolve.
This does not mean that computers of great intelligence in special
areas will not be developed. It may be quantitatively and qualitatively
difficult for AI to duplicate the human brain, but intelligent
computers may be developed by other means, just as IBM managed
to build the world's best chess player by combining clever software
with extraordinarily fast computer chips. But we should not expect
a computer developed in this way to have all the mental capacities
of humans, and we certainly should not expect it to have anything
like human consciousness, which Section 6.1 suggested is intrinsically
tied to human emotions and hence to our peculiar brain chemistry.
8. Conclusion
My arguments that neurochemistry matters
to mental computation are not meant to show that computational
models of the mind have to be at the molecular level. As I stated
at the end of Section 2, models are like maps in that various
levels of detail are useful for different purposes. Symbolic models
of high-level inference and neural network models with and without
spiking neurons have proven very useful in explaining many facets
of cognition, and I have no doubt that they will continue to be
useful. Cognitive science benefits from a combination of many
different fields and methodologies, with different researchers
attacking the problem of understanding mind and intelligence at
different levels.
Without recommending abandonment of the techniques of computational
modeling that have served cognitive science well, it is nevertheless
evident that there are new possibilities for enhancing understanding
of mind by working more at the molecular level. Consider, for
example, the computational study of emergent properties of chemical
pathways conducted by Bhalla and Iyengar (1999), including integration
of signals across multiple time scales and self-sustaining feedback
loops. It is possible that computational modeling of brain activity
at the molecular level will discover additional emergent properties
that are important for understanding some of the most currently
intractable problems in cognitive science, such as the origins
of emotional consciousness. Hence without abandoning traditional
concerns and methods, it may be time for psychology and the philosophy
of mind to become, like current biology and medicine, molecular.
Acknowledgents.
I am grateful to Baljinder Sahdra and Zhu Jing for helpful suggestions,
and especially to Chris Eliasmith for skeptical comments. This
research is supported by the Natural Sciences and Engineering
Research Council of Canada.
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