Latash, 2010: "Motor Control Theories and Their Applications"

[From Bruce Abbott (2013.12.29.2225 EST)]

Rick Marken (2013.12.29.1600)–

Bruce Abbott (2013.28.1745 EST)

BA: I’ve been doing some on-line reading of the literature on the equilibrium point (EP) model and can see why Latash et al. would not be aware of any connection of their model with PCT.

RM: It’s not that they are “not aware” of a connection; they explicitly reject a connection because they reject control theory.

BA: In fact, I’ve found it difficult to understand their model, given the descriptions I’ve read of it. Thus far I have not seen a discussion of how the “commands” of the system are derived; it could very well be an open-loop system at that level, or maybe not.

RM: I’m pretty sure it’s an open loop model. Their “commands” are not like reference signals because they not specifications for input. Rather, they are commands for output – they cause the tensions in the muscles (outputs) that determine the equilibrium point of the limbs to which the muscles are attached.

BA: It’s like presenting a PCT diagram of a first-order control system without indicating what determines the reference signal values – they could be determined by the output of an open-loop or closed-loop system.

RM: I think it’s like presenting a PCT diagram of a first order control system without the first order perception and comparator. In other words, it’s nothing like a PCT diagram; it’s a diagram of a motor output system and I can only assume that they persist in going with this approach because they want to explain control in lineal causal terms. The relationship of Latish et at to PCT is one of complete opposition.

BA: You might be able to gain some clarity about the EP model by reading at least a portion of a paper by Perrier, Ostry, and Laboissiere (1996, which is available online at http://www.psych.mcgill.ca/labs/mcl/pdf/jshr96final.pdf.

BA: These authors discuss control jaw movements rather than arm movements, but they do illustrate how the EP model resists the effect of loads. They offer a simplified description for a single (closer) muscle first, then go on to treat antagonistic muscles. I’ve reproduced their Figure 1 below, which refers to the single-muscle case:

BA: In (a) the face is horizontal so the jaw is unloaded and the individual is holding the jaw slightly open. The bar graph shows the contributions of central input and afferent input (from muscle stretch receptors) to motor neuron depolarization, which determines the strength of muscle contraction. The line graph below the bar graph indicates the equilibrium-point functions, the rightmost being the curve determined by the current central input (λ). (a) on that curve refers to the equilibrium point established under the condition shown in the (a) figure of the skull and jaw.

BA: In Figure 1b, the face is not vertical and a downward force is being exerted on the lower jaw by gravity. With no change in the central input, the jaw is pulled open, stretching the muscle that closes the jaw, which increases the input from the stretch receptor. There is more total depolarization as shown in the bar graph at (b), so muscle force increases, thus resisting further opening of the jaw. Because the central input is unchanged, we are still on the same EP curve but now at (b) as shown in the lower graph.

BA: In Figure 1c, central input changes to establish a new EP curve (λ’ in the lower graph) As the jaw closes, the muscle shortens, so afferent input from the muscle spindles decreases to the same level seen in Figure 1a, but there is now more central input, so muscle length decreases while maintaining the same level of force and continuing to balance the force generated against the force due to load.

BA: You can get a more detailed description by reading the paper, especially the section labeled “Basic Control Mechanisms.”

Bruce

[From Rick Marken (2013.12.30.1100)]

Bruce Abbott (2013.12.29.2225 EST)--

BA: You might be able to gain some clarity about the EP model by reading at least a portion of a paper by Perrier, Ostry, and Laboissiere (1996, which is available online at http://www.psych.mcgill.ca/labs/mcl/pdf/jshr96final.pdf .

RM: Thanks Bruce. This is very interesting. Based on the equations in
the Appendix and the flow diagram of the model in Figure 3 (p. 46) it
seems like this is just a good old fashioned control model. The
controlled variable seems to be muscle length. Equation 1 is the
"system equation" giving output (muscle activation, A) as a function
of error (the difference between muscle length (l) and the reference
for muscle length (lambda). It's a threshold equation which just means
it's a one way control system (which must be true of all physiological
control systems since there is no negative neural firing rate); output
is generated, in this case, only when muscle length is greater than
the reference (they add in the derivative of length which is
equivalent to raising or lowering the reference for length when muscle
length is increasing or decreasing, respectively). So the output (A)
must cause contraction of the muscle. And that's what seems to be
shown by equation 4; activation, A, leads to an exponential increase
in force on the limbs, M, resulting from the decrease in muscle
length. Unfortunately, they don't close the loop (showing the
relationship between output (A) and input (length, l), nor so they
show disturbances (such as muscle fatigue) that could influence the
effect of A on l. That is, they should have an equation like:

l = kA - D

to make it a clear, closed-loop control model of muscle length
control. But based on Figure 3 it looks like they are describing a
closed loop control model of muscle length.

Actually, now that I look at Figure 3 I see that it is not a closed
loop model at all. Oops. There is no control of muscle length at all.
Equation 1 is really just an output generation equation; it causes the
muscle to contract; this contraction leads to changes in muscle
length, which is what is shown by what I though were the "feedback"
lines in Fig. 3. In fact those feedback lines are just the lines
showing the causal effect of muscle contraction on muscle length. The
two arrows going into the little "muscle" bix is what fooled me; I
took the muscle box to be a comparator; but it's not. the lambda into
to the box drives muscle contraction, which shortens or lengthens the
muscle via the resulting movements of the jaw (the other arrow coming
into the box).

So the lambda model is what I thought it was in the first place; an
output generation mode, not a control model. It will fail miserably as
soon as you include disturbances to the apparently controlled
variable.

Best

Rick

···

--
Richard S. Marken PhD
www.mindreadings.com

The only thing that will redeem mankind is cooperation.
                                                   -- Bertrand Russell

[From Bruce Abbott (2013.12.30.1930 EST)]

RM: Rick Marken (2013.12.30.1100)]

BA Bruce Abbott (2013.12.29.2225 EST)--

BA: You might be able to gain some clarity about the EP model by reading at

least a portion of a paper by Perrier, Ostry, and Laboissiere (1996, which
is available online at
http://www.psych.mcgill.ca/labs/mcl/pdf/jshr96final.pdf .

RM: Thanks Bruce. This is very interesting. Based on the equations in the
Appendix and the flow diagram of the model in Figure 3 (p. 46) it seems like
this is just a good old fashioned control model. The controlled variable
seems to be muscle length. Equation 1 is the "system equation" giving output
(muscle activation, A) as a function of error (the difference between muscle
length (l) and the reference for muscle length (lambda). It's a threshold
equation which just means it's a one way control system (which must be true
of all physiological control systems since there is no negative neural
firing rate); output is generated, in this case, only when muscle length is
greater than the reference (they add in the derivative of length which is
equivalent to raising or lowering the reference for length when muscle
length is increasing or decreasing, respectively). So the output (A) must
cause contraction of the muscle. And that's what seems to be shown by
equation 4; activation, A, leads to an exponential increase in force on the
limbs, M, resulting from the decrease in muscle length. Unfortunately, they
don't close the loop (showing the relationship between output (A) and input
(length, l), nor so they show disturbances (such as muscle fatigue) that
could influence the effect of A on l. That is, they should have an equation
like:

RM: l = kA - D

RM: to make it a clear, closed-loop control model of muscle length control.
But based on Figure 3 it looks like they are describing a closed loop
control model of muscle length.

RM: Actually, now that I look at Figure 3 I see that it is not a closed loop
model at all. Oops. There is no control of muscle length at all.
Equation 1 is really just an output generation equation; it causes the
muscle to contract; this contraction leads to changes in muscle length,
which is what is shown by what I though were the "feedback"
lines in Fig. 3. In fact those feedback lines are just the lines showing the
causal effect of muscle contraction on muscle length. The two arrows going
into the little "muscle" bix is what fooled me; I took the muscle box to be
a comparator; but it's not. the lambda into to the box drives muscle
contraction, which shortens or lengthens the muscle via the resulting
movements of the jaw (the other arrow coming into the box).

RM: So the lambda model is what I thought it was in the first place; an
output generation mode, not a control model. It will fail miserably as soon
as you include disturbances to the apparently controlled variable.

BA: If you examine the larger portion of Figure 3 (showing a representation
of the skull and jaw at right, you will see an arrow labeled "muscle lengths
and rate of change in muscle lengths" running back to the muscles. These
changes alter the outputs of the muscle spindles, contributing to motor
neuron depolarization thresholds, so that a lengthening muscle generates a
feedback signal that opposes the lengthening. Any disturbance that began to
stretch the muscle beyond the "equilibrium point" established by the lambda
control inputs generates this opposing force.

Bruce

Thanks Bruce. The irony is that when Bill explained the similarities to PCT to Latash, Feldman and colleagues they refused to see any similarities at all. I think Rick might be doing this from the PCT side! I would just like to see how PCT would tidy this up properly and go beyond the diagrams in B:CP...
Warren

···

Sent from my iPhone

On 31 Dec 2013, at 00:56, Bruce Abbott <bbabbott@FRONTIER.COM> wrote:

[From Bruce Abbott (2013.12.30.1930 EST)]

RM: Rick Marken (2013.12.30.1100)]

BA Bruce Abbott (2013.12.29.2225 EST)--

BA: You might be able to gain some clarity about the EP model by reading at

least a portion of a paper by Perrier, Ostry, and Laboissiere (1996, which
is available online at
http://www.psych.mcgill.ca/labs/mcl/pdf/jshr96final.pdf .

RM: Thanks Bruce. This is very interesting. Based on the equations in the
Appendix and the flow diagram of the model in Figure 3 (p. 46) it seems like
this is just a good old fashioned control model. The controlled variable
seems to be muscle length. Equation 1 is the "system equation" giving output
(muscle activation, A) as a function of error (the difference between muscle
length (l) and the reference for muscle length (lambda). It's a threshold
equation which just means it's a one way control system (which must be true
of all physiological control systems since there is no negative neural
firing rate); output is generated, in this case, only when muscle length is
greater than the reference (they add in the derivative of length which is
equivalent to raising or lowering the reference for length when muscle
length is increasing or decreasing, respectively). So the output (A) must
cause contraction of the muscle. And that's what seems to be shown by
equation 4; activation, A, leads to an exponential increase in force on the
limbs, M, resulting from the decrease in muscle length. Unfortunately, they
don't close the loop (showing the relationship between output (A) and input
(length, l), nor so they show disturbances (such as muscle fatigue) that
could influence the effect of A on l. That is, they should have an equation
like:

RM: l = kA - D

RM: to make it a clear, closed-loop control model of muscle length control.
But based on Figure 3 it looks like they are describing a closed loop
control model of muscle length.

RM: Actually, now that I look at Figure 3 I see that it is not a closed loop
model at all. Oops. There is no control of muscle length at all.
Equation 1 is really just an output generation equation; it causes the
muscle to contract; this contraction leads to changes in muscle length,
which is what is shown by what I though were the "feedback"
lines in Fig. 3. In fact those feedback lines are just the lines showing the
causal effect of muscle contraction on muscle length. The two arrows going
into the little "muscle" bix is what fooled me; I took the muscle box to be
a comparator; but it's not. the lambda into to the box drives muscle
contraction, which shortens or lengthens the muscle via the resulting
movements of the jaw (the other arrow coming into the box).

RM: So the lambda model is what I thought it was in the first place; an
output generation mode, not a control model. It will fail miserably as soon
as you include disturbances to the apparently controlled variable.

BA: If you examine the larger portion of Figure 3 (showing a representation
of the skull and jaw at right, you will see an arrow labeled "muscle lengths
and rate of change in muscle lengths" running back to the muscles. These
changes alter the outputs of the muscle spindles, contributing to motor
neuron depolarization thresholds, so that a lengthening muscle generates a
feedback signal that opposes the lengthening. Any disturbance that began to
stretch the muscle beyond the "equilibrium point" established by the lambda
control inputs generates this opposing force.

Bruce

[From Rick Marken (2013.12.31.0850)]

WM: Thanks Bruce. The irony is that when Bill explained the similarities to PCT to Latash, Feldman and colleagues they refused to see any similarities at all. I think Rick might be doing this from the PCT side! I would just like to see how PCT would tidy this up properly and go beyond the diagrams in B:CP...

RM: Do you have a copy of Bill's explanation of the similarities of
the Latash, Feldman theory to PCT? I'd just like to know which
similarities I'm refusing to see before I continue to make a fool of
myself by continuing to see (and point out) only dissimilarities.

My guess is that the similarities Bill saw between PCT and Patash,
Feldman are the same as the similarities he pointed out between PCT
and behaviorism: they are called the "behavioral illusion". But maybe
I'm wrong. If there is no written copy of Bill's explanation of the
similarities between the Latash, Feldman theory and PCT maybe you
could just summarize them yourself. Or maybe Bruce could. Or Martin.
You guys are obviously much better at seeing these similarities than I
am.

Best

Rick

···

On Tue, Dec 31, 2013 at 5:27 AM, Warren Mansell <wmansell@gmail.com> wrote:

Warren

Sent from my iPhone

On 31 Dec 2013, at 00:56, Bruce Abbott <bbabbott@FRONTIER.COM> wrote:

[From Bruce Abbott (2013.12.30.1930 EST)]

RM: Rick Marken (2013.12.30.1100)]

BA Bruce Abbott (2013.12.29.2225 EST)--

BA: You might be able to gain some clarity about the EP model by reading at

least a portion of a paper by Perrier, Ostry, and Laboissiere (1996, which
is available online at
http://www.psych.mcgill.ca/labs/mcl/pdf/jshr96final.pdf .

RM: Thanks Bruce. This is very interesting. Based on the equations in the
Appendix and the flow diagram of the model in Figure 3 (p. 46) it seems like
this is just a good old fashioned control model. The controlled variable
seems to be muscle length. Equation 1 is the "system equation" giving output
(muscle activation, A) as a function of error (the difference between muscle
length (l) and the reference for muscle length (lambda). It's a threshold
equation which just means it's a one way control system (which must be true
of all physiological control systems since there is no negative neural
firing rate); output is generated, in this case, only when muscle length is
greater than the reference (they add in the derivative of length which is
equivalent to raising or lowering the reference for length when muscle
length is increasing or decreasing, respectively). So the output (A) must
cause contraction of the muscle. And that's what seems to be shown by
equation 4; activation, A, leads to an exponential increase in force on the
limbs, M, resulting from the decrease in muscle length. Unfortunately, they
don't close the loop (showing the relationship between output (A) and input
(length, l), nor so they show disturbances (such as muscle fatigue) that
could influence the effect of A on l. That is, they should have an equation
like:

RM: l = kA - D

RM: to make it a clear, closed-loop control model of muscle length control.
But based on Figure 3 it looks like they are describing a closed loop
control model of muscle length.

RM: Actually, now that I look at Figure 3 I see that it is not a closed loop
model at all. Oops. There is no control of muscle length at all.
Equation 1 is really just an output generation equation; it causes the
muscle to contract; this contraction leads to changes in muscle length,
which is what is shown by what I though were the "feedback"
lines in Fig. 3. In fact those feedback lines are just the lines showing the
causal effect of muscle contraction on muscle length. The two arrows going
into the little "muscle" bix is what fooled me; I took the muscle box to be
a comparator; but it's not. the lambda into to the box drives muscle
contraction, which shortens or lengthens the muscle via the resulting
movements of the jaw (the other arrow coming into the box).

RM: So the lambda model is what I thought it was in the first place; an
output generation mode, not a control model. It will fail miserably as soon
as you include disturbances to the apparently controlled variable.

BA: If you examine the larger portion of Figure 3 (showing a representation
of the skull and jaw at right, you will see an arrow labeled "muscle lengths
and rate of change in muscle lengths" running back to the muscles. These
changes alter the outputs of the muscle spindles, contributing to motor
neuron depolarization thresholds, so that a lengthening muscle generates a
feedback signal that opposes the lengthening. Any disturbance that began to
stretch the muscle beyond the "equilibrium point" established by the lambda
control inputs generates this opposing force.

Bruce

--
Richard S. Marken PhD
www.mindreadings.com

The only thing that will redeem mankind is cooperation.
                                                   -- Bertrand Russell

[From Rick Marken (2013.12.31.1230)]

Bruce Abbott (2013.12.30.1930 EST)--

RM: So the lambda model is what I thought it was in the first place; an
output generation mode, not a control model. It will fail miserably as soon
as you include disturbances to the apparently controlled variable.

BA: If you examine the larger portion of Figure 3 (showing a representation
of the skull and jaw at right, you will see an arrow labeled "muscle lengths
and rate of change in muscle lengths" running back to the muscles. These
changes alter the outputs of the muscle spindles, contributing to motor
neuron depolarization thresholds, so that a lengthening muscle generates a
feedback signal that opposes the lengthening. Any disturbance that began to
stretch the muscle beyond the "equilibrium point" established by the lambda
control inputs generates this opposing force.

RM: You're right. If I read the arrows as representing neural signal
paths and the boxes as comparators (points of synapse), then the
central command (lambda) is a reference signal and "length" and "rate
of change in length" are perceptual signals entering a comparator
function called "Length Dependent". The comparator is presumably what
carries out the calculation of the muscle activation signal per
equation 1 (or 3, which takes neural delays into account). Actually
the output of the comparator (A in equations 1 and 3) goes through a
series of subsequent functions (called "Time Dependent " and Velocity
Dependent") but eventually the output signal produces a force, which
has feedback effects on the "length" and "rate of change in length"
perceptions.

So if I'm reading this right this is precisely a PCT model of control
of a perception of a combination of muscle length and rate of change
in muscle length. This perception is being controlled relative to
lambda, the reference input. Their diagram does leave out some of the
nice functional labels that we would include in a PCT model,
particularly the "length" and "rate of change in length" perceptual
functions; it also leaves out disturbances to the controlled variable.
Disturbances are forces that affect the muscle length/rate of change
perception that are independent of the forces produced by the control
system. So those forces would enter from outside the "Force generating
mechanisms" box and join with the forces generated by the control
system at the circle containing the plus sign, adding to the forces
that have an affect on the controlled variable (muscle length/rate of
change). Maybe this is what Bill Powers saw as their model being
similar to PCT.

So I would have to agree that this model is not only similar to PCT;
it is exactly PCT. At least, that's true of the diagram. So it's not
an equilibrium model; it's a closed loop control model, just like PCT.
It's a muscle length control model, like the one Bill implemented in
the little man, except that it adds the derivative of muscle length
into the percpetion for some reason.

but I see a lot (in terms of clarity) that the people who developed
this model could learn from PCT.

I've just downloaded their more "detailed" presentation of the model
--Laboissiere , Ostry and Feldman ( in press ) -- because the math
that they show in the Appendix to the subject paper doesn't seem to
match the model shown in the diagram. The main problem is that the
math doesn't describe the closed loop that is implied by the diagram.
Because there is no feedback equation showing the dependence of
length/rate of change in length on the output of the system the
length/rate of change perception can be treated as though it were an
independent (rather than a controlled) variable. I suspect that this
is what is going on because the behavior that they show in figure 5
could be produced by a filtered version of the "command signal" and
the series of functions they show in the diagram in Figure 3 looks a
lot like a cascade of filters.

I've scanned the detailed model paper and I can't find any feedback
function stuff in their either.

I guess I'm at the point where I can't tell what the heck this paper
shows. The diagram suggests that they have a control model of muscle
length (with the addition of the derivative of muscle length into the
perception). Their math suggests that they have an output generation
model, where the output generated by the lambda command signal is
filtered by the processes that turn the difference between lambda and
length/rate of change in length into the forces that change length. If
it's a PCT model, then the main question would be whether they got the
controlled variable right; is it really length/rate of change in
length that is controlled or just length? This should be easily
testable. It it's an output generation model then it is completely
different than PCT and it should be easy to show that it fails where
PCT succeeds.

So I suggest that, rather than arguing about whether these equilibrium
theory folks have anything to teach (or learn from) us, we should just
implement their model as a computer program. Once we agree that we've
got their model right we can go on to see what we can learn from it. A
computer model would (I think) go a long way to helping me see how
their model works and what it does.

I can tell you one thing their model does for me; it makes me long for
the clarity and simplicity of PCT.

Best

Rick

···

from my point of view I see nothing PCT has to learn from this model;
--
Richard S. Marken PhD
www.mindreadings.com

The only thing that will redeem mankind is cooperation.
                                                   -- Bertrand Russell

Hi there, I think Dag is poised to make the thread that Bill had with the 'threshold' people accessible to CSG. I think we each differ on style and I value your purist approach Rick - it gives me strength when debating with my colleagues. I guess I think that we might still be able to magpie useful ideas from other peoples' work whilst keeping the fundamental explanatory power of PCT...
Warren

···

Sent from my iPhone

On 31 Dec 2013, at 16:49, Richard Marken <rsmarken@GMAIL.COM> wrote:

[From Rick Marken (2013.12.31.0850)]

On Tue, Dec 31, 2013 at 5:27 AM, Warren Mansell <wmansell@gmail.com> wrote:
WM: Thanks Bruce. The irony is that when Bill explained the similarities to PCT to Latash, Feldman and colleagues they refused to see any similarities at all. I think Rick might be doing this from the PCT side! I would just like to see how PCT would tidy this up properly and go beyond the diagrams in B:CP...

RM: Do you have a copy of Bill's explanation of the similarities of
the Latash, Feldman theory to PCT? I'd just like to know which
similarities I'm refusing to see before I continue to make a fool of
myself by continuing to see (and point out) only dissimilarities.

My guess is that the similarities Bill saw between PCT and Patash,
Feldman are the same as the similarities he pointed out between PCT
and behaviorism: they are called the "behavioral illusion". But maybe
I'm wrong. If there is no written copy of Bill's explanation of the
similarities between the Latash, Feldman theory and PCT maybe you
could just summarize them yourself. Or maybe Bruce could. Or Martin.
You guys are obviously much better at seeing these similarities than I
am.

Best

Rick

Warren

Sent from my iPhone

On 31 Dec 2013, at 00:56, Bruce Abbott <bbabbott@FRONTIER.COM> wrote:

[From Bruce Abbott (2013.12.30.1930 EST)]

RM: Rick Marken (2013.12.30.1100)]

BA Bruce Abbott (2013.12.29.2225 EST)--

BA: You might be able to gain some clarity about the EP model by reading at

least a portion of a paper by Perrier, Ostry, and Laboissiere (1996, which
is available online at
http://www.psych.mcgill.ca/labs/mcl/pdf/jshr96final.pdf .

RM: Thanks Bruce. This is very interesting. Based on the equations in the
Appendix and the flow diagram of the model in Figure 3 (p. 46) it seems like
this is just a good old fashioned control model. The controlled variable
seems to be muscle length. Equation 1 is the "system equation" giving output
(muscle activation, A) as a function of error (the difference between muscle
length (l) and the reference for muscle length (lambda). It's a threshold
equation which just means it's a one way control system (which must be true
of all physiological control systems since there is no negative neural
firing rate); output is generated, in this case, only when muscle length is
greater than the reference (they add in the derivative of length which is
equivalent to raising or lowering the reference for length when muscle
length is increasing or decreasing, respectively). So the output (A) must
cause contraction of the muscle. And that's what seems to be shown by
equation 4; activation, A, leads to an exponential increase in force on the
limbs, M, resulting from the decrease in muscle length. Unfortunately, they
don't close the loop (showing the relationship between output (A) and input
(length, l), nor so they show disturbances (such as muscle fatigue) that
could influence the effect of A on l. That is, they should have an equation
like:

RM: l = kA - D

RM: to make it a clear, closed-loop control model of muscle length control.
But based on Figure 3 it looks like they are describing a closed loop
control model of muscle length.

RM: Actually, now that I look at Figure 3 I see that it is not a closed loop
model at all. Oops. There is no control of muscle length at all.
Equation 1 is really just an output generation equation; it causes the
muscle to contract; this contraction leads to changes in muscle length,
which is what is shown by what I though were the "feedback"
lines in Fig. 3. In fact those feedback lines are just the lines showing the
causal effect of muscle contraction on muscle length. The two arrows going
into the little "muscle" bix is what fooled me; I took the muscle box to be
a comparator; but it's not. the lambda into to the box drives muscle
contraction, which shortens or lengthens the muscle via the resulting
movements of the jaw (the other arrow coming into the box).

RM: So the lambda model is what I thought it was in the first place; an
output generation mode, not a control model. It will fail miserably as soon
as you include disturbances to the apparently controlled variable.

BA: If you examine the larger portion of Figure 3 (showing a representation
of the skull and jaw at right, you will see an arrow labeled "muscle lengths
and rate of change in muscle lengths" running back to the muscles. These
changes alter the outputs of the muscle spindles, contributing to motor
neuron depolarization thresholds, so that a lengthening muscle generates a
feedback signal that opposes the lengthening. Any disturbance that began to
stretch the muscle beyond the "equilibrium point" established by the lambda
control inputs generates this opposing force.

Bruce

--
Richard S. Marken PhD
www.mindreadings.com

The only thing that will redeem mankind is cooperation.
                                                  -- Bertrand Russell