W. Ross Ashby: Digital Archive

[From Matti Kolu (2014.01.23.0845 CET)]

Ashby's journals are published online. He kept indexes for certain
topics such as "The Multistable System" which have been linked to the
appropriate journal pages on the website.

"William Ross Ashby was a British pioneer in the fields of cybernetics
and systems theory. He is best known for his Law of Requisite Variety,
for his 1952 book Design for a Brain, 1956 book An Introduction to
Cybernetics, and for building the Homeostat device.

In 2003, Ross's daughters gave his archive materials to The British
Library, London. Then, in 2004, at the end of the W. Ross Ashby
Centenary Conference, they announced that they would make Ross's
Journal available on the Internet. This web site fulfills that
promise, making this previously unpublished work available on-line."

http://www.rossashby.info/

Matti

[From Matti Kolu (2014.01.23.1910 CET)]

"The concept of "negative feedback" is just too simple to be worth anything."
-- W. Ross Ashby, Journal (1928-1972), p 2512, volume 11, 1948.

Matti

I did wonder whether Ashby was a bit over-rated! He seems to have god-like status among unrepentant cyberneticists....

···

Sent from my iPhone

On 23 Jan 2014, at 18:09, Matti Kolu <matti.kolu@GMAIL.COM> wrote:

[From Matti Kolu (2014.01.23.1910 CET)]

"The concept of "negative feedback" is just too simple to be worth anything."
-- W. Ross Ashby, Journal (1928-1972), p 2512, volume 11, 1948.

Matti

[From Bruce Abbott (2014.01.23.1845]

I imagine Ashby would be mortified to know that his private notes were made
available to the general public. He may have held that opinion briefly in
1948 (based on who knows what that was available to him at the time), but
his subsequent published writings show that he did not maintain that opinion
for long.

Have you gone through his Introduction to Cybernetics (1957)? Using only
discrete mathematics he teaches in a sometimes amusing way the basic
concepts of "control and communication," including many exercises for the
reader to work. And don't forget that Bill borrowed Ashby's concept of
essential variables as the basis of his (Bill's) reorganizing system.

Bruce

···

-----Original Message-----
From: Control Systems Group Network (CSGnet)
[mailto:CSGNET@LISTSERV.ILLINOIS.EDU] On Behalf Of Matti Kolu
Sent: Thursday, January 23, 2014 1:10 PM
To: CSGNET@LISTSERV.ILLINOIS.EDU
Subject: Re: W. Ross Ashby: Digital Archive

[From Matti Kolu (2014.01.23.1910 CET)]

"The concept of "negative feedback" is just too simple to be worth
anything."
-- W. Ross Ashby, Journal (1928-1972), p 2512, volume 11, 1948.

Matti
-----
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Checked by AVG - www.avg.com
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[From Matti Kolu (2014.01.25.1430 CET)]

Bruce Abbott (2014.01.23.1845)--

He may have held that opinion briefly in
1948 (based on who knows what that was available to him at the time), but
his subsequent published writings show that he did not maintain that opinion
for long.

Sorry, I did not link to the journal page itself -- perhaps I should
have. The journals resemble lab notebooks more than diaries. The
format and the context make it clear that no one should interpret the
writings as anything more than reflections of what he thought at the
specific time that he wrote them. I have not read his "Introduction to
Cybernetics" and I probably won't for quite some time. I am
intentionally treating a lot of the stuff from the 1950s and 1960s in
a cursorily manner, so as to not get bogged down by now irrelevant
material. I think I will eventually end up reading the book, once my
technical understanding of control theory is better and I can
independently judge what is good from what is not. I do not have the
necessary experience and skills to do that at the moment.

Powers wrote this about Ashby in December 2012:

In short, we know that negative feedback control can be used to
improve on the performance of even the very best possible open-loop
systems in almost every respect. Ashby's assumption that there is ANY
means of achieving perfect control was erroneous, making his whole
argument pointless.

Aside from the fact that many of us (including me) found inspiration
in Ashby's writings and ideas and clever devices, we have to conclude
that he set a large number of people onto a false path which they
still follow and which still constitutes a formidable blockade in the
way of acceptance of PCT. It is our unpleasant duty to tell others
that this perfectly nice intelligent man did more to harm science
than to help it.

I do not know to which extent Powers was correct in his verdict. From
another post I got the impression that he might have formed his
opinion based on Ashby's early works. But for now I am treating
Ashby's writings with some caution, being a bit more worried about
starting to follow those possibly false paths than about missing the
occasional (or frequent) insight(s) that can be found in his works.

But I did find the "too simple" statement interesting. The same
objection seems to occasionally be raised against PCT. It was also
what Edward Ihnatowicz thought about his life-like robots, which he
created after first having dabbled with control systems and analogue
computers. "Negative feedback" does at a first glance seem trivial.
People who come in contact with e.g. servo-mechanisms often tend to
note the life-like effects that arise so easily... just to go on and
ignore that observation, because the underlying circuitry isn't
complicated enough.

This is something that is worth taking seriously by those who are
interested in promulgating the perceptual control perspective. It
seems to me that the objection can be neutralized or countered before
it arises in the mind of the reader or the audience member.

Matti

[From Bruce Abbott (2014.01.27.1900 EST)]

Just returned from watching a live performance of "Phantom" in Chicago and
found your message:

Matti Kolu (2014.01.25.1430 CET)]

Bruce Abbott (2014.01.23.1845)--

He may have held that opinion briefly in
1948 (based on who knows what that was available to him at the time),
but his subsequent published writings show that he did not maintain
that opinion for long.

MK: Sorry, I did not link to the journal page itself -- perhaps I should
have. The journals resemble lab notebooks more than diaries. The format and
the context make it clear that no one should interpret the writings as
anything more than reflections of what he thought at the specific time that
he wrote them. I have not read his "Introduction to Cybernetics" and I
probably won't for quite some time. I am intentionally treating a lot of the
stuff from the 1950s and 1960s in a cursorily manner, so as to not get
bogged down by now irrelevant material. I think I will eventually end up
reading the book, once my technical understanding of control theory is
better and I can independently judge what is good from what is not. I do not
have the necessary experience and skills to do that at the moment.

MK: Powers wrote this about Ashby in December 2012:

In short, we know that negative feedback control can be used to
improve on the performance of even the very best possible open-loop
systems in almost every respect. Ashby's assumption that there is ANY
means of achieving perfect control was erroneous, making his whole
argument pointless.

Aside from the fact that many of us (including me) found inspiration
in Ashby's writings and ideas and clever devices, we have to conclude
that he set a large number of people onto a false path which they
still follow and which still constitutes a formidable blockade in the
way of acceptance of PCT. It is our unpleasant duty to tell others
that this perfectly nice intelligent man did more to harm science than
to help it.

I think that Bill mistook Ashby as ADVOCATING feed-forward control over
negative feedback control, but that is not how I read Ashby. In my opinion,
Ashby simply was pointing out that, with feed forward, "errorless" control
is theoretically possible, whereas in negative-feedback control, some degree
of error is actually REQUIRED in order to produce the output that acts
against the effect of the disturbance on the controlled variable. IF you
could accurately sense the source of the disturbance far enough "upstream"
of its effect on the variable to be controlled, and IF you could generate a
second variable that varied as the inverse of the disturbance variable, THEN
you could apply this opposing variable to the variable to be controlled, in
phase with the disturbing variable, and exactly cancel the disturbing
variable's effect.

Bill correctly noted that any real-world application of this model would not
yield perfect control because there would always be inaccurate sensing,
system noise, phase shifts, and so on that would prevent the opposing
variable from being an exact mirror of the disturbance variable. He worried
that the idea of errorless control would lead folks to reject negative
feedback control in favor of feed forward control as the basic control
model. It's easy to conclude that Bill rejected feed forward models, but in
fact he did not rule them out entirely, although he thought that the
additional complexity imposed by measuring disturbances and then generating
an opposing inverse variable made it likely that feed forward mechanisms do
not play a significant role.

In fact, feed forward is used in biological systems, alongside feedback
control. As one example, regulation of body temperature doesn't depend
merely on sensing changes in core body temperature and acting to oppose
them, although there is a mechanism in the hypothalamus that does exactly
that. Action against changes in core body temperature is needed
infrequently because surface sensors detect when skin temperatures are
falling or rising and bring into play such outputs as shivering or sweating
long before core body temperature begins to change. If skin temperature is
falling, for example, this may provide a kind of "early warning" signal that
gets the temperature regulation system active to oppose those changes long
before core body temperature starts to change. Feed forward doesn't have to
be perfect to be of benefit.

Bruce

[From Rick Marken (2014.01.27.1710)]

···

Bruce Abbott (2014.01.27.1900 EST)–

BA: Just returned from watching a live performance of “Phantom” in Chicago

RM: That is so 1990s;-)

BA: In fact, feed forward is used in biological systems, alongside feedback

control.

RM: It might not be a fact.

As one example, regulation of body temperature doesn’t depend

merely on sensing changes in core body temperature and acting to oppose

them, although there is a mechanism in the hypothalamus that does exactly

that. Action against changes in core body temperature is needed

infrequently because surface sensors detect when skin temperatures are

falling or rising and bring into play such outputs as shivering or sweating

long before core body temperature begins to change. If skin temperature is

falling, for example, this may provide a kind of “early warning” signal that

gets the temperature regulation system active to oppose those changes long

before core body temperature starts to change.

RM: Or it could be negative feedback control of the rate of change in skin temperature.

BA: Feed forward doesn’t have to be perfect to be of benefit.

RM: It really doesn’t even have to exist;-) But whether it does or not is an empirical question. But it’s always good to be cautious about apparent S-R relationships between input variables (like changes in skin temperature and output variables (like shivering and sweating), especially when the output clearly has an effect on the input, as it does in this case.

Best

Rick

Bruce


Richard S. Marken PhD
www.mindreadings.com
The only thing that will redeem mankind is cooperation.

                                               -- Bertrand Russell

[From Bruce Abbott (2014.01.28.1115 EST)]

Rick Marken (2014.01.27.1710) –

Bruce Abbott (2014.01.27.1900 EST)

BA: Just returned from watching a live performance of “Phantom” in Chicago

RM: That is so 1990s;-)

BA: An oldie but a goodie; not the first time I’ve seen it!

BA: In fact, feed forward is used in biological systems, alongside feedback
control.

RM: It might not be a fact.

BA: Scenario 1: Someone yells “Rick, Duck!” You instantly dive for the floor, just as a huge wrecking ball careens through your living room, barely missing you. Result: you are still alive and well.

BA: Scenario 2: Someone yells “Rick, Duck!” But those words do not disturb any variable you are currently controlling, so you just stand there. As soon as the wrecking ball strikes your body, a sudden, massive error develops for a number of your controlled perceptions, including such things as maintaining your former posture and maintaining pain levels somewhere near zero. Not only are your muscles and frame too weak to resist the disturbance, but you didn’t have enough time to begin to counteract the applied forces.

BA: Scenario 1: feed forward. Scenario 2: error-based feed back. I know which system I’d prefer to be using to counter the potential effects of the disturbance in this example!

BA: As one example, regulation of body temperature doesn’t depend
merely on sensing changes in core body temperature and acting to oppose
them, although there is a mechanism in the hypothalamus that does exactly
that. Action against changes in core body temperature is needed
infrequently because surface sensors detect when skin temperatures are
falling or rising and bring into play such outputs as shivering or sweating
long before core body temperature begins to change. If skin temperature is
falling, for example, this may provide a kind of “early warning” signal that
gets the temperature regulation system active to oppose those changes long
before core body temperature starts to change.

RM: Or it could be negative feedback control of the rate of change in skin temperature.

BA: Perhaps. It’s an empirical question.

BA: Feed forward doesn’t have to be perfect to be of benefit.

RM: It really doesn’t even have to exist;-) But whether it does or not is an empirical question. But it’s always good to be cautious about apparent S-R relationships between input variables (like changes in skin temperature and output variables (like shivering and sweating), especially when the output clearly has an effect on the input, as it does in this case.

BA: I don’t find anything to disagree with there. But we shouldn’t be ruling out other possibilities unless there is empirical evidence against them.

Bruce

[From Rick Marken (2014.01.28.1150)]

Bruce Abbott (2014.01.28.1115 EST)--

RM: [Feedforward] might not be a fact.

BA: Scenario 1: Someone yells "Rick, Duck!" You instantly dive for the floor, just as a huge wrecking ball careens through your living room, barely missing you. Result: you are still alive and well.

RM: I think you have to explain to me what feedforward control _is_.
Your scenario 1 doesn;t seem to involve feedforward control, at least
as I understand it. My understanding is that feedforward control
involves the system acting on it's own prediction of the future state
of a disturbance to a controlled variable in order to preemptively
deal with the effects of that disturbance. In Scenario 1 it's the
person who yells "Duck" rather than, Rick, the person controlling for
not getting hit, who is doing the predicting. The prediction will lead
to preemptive action if, as you say, Rick is controlling for ducking
when someone yells "duck". SO all there is is negative feedback
control involved in this scenario as far as I can see; the person who
yells dick is controlling for Rick not getting hurt and Rick is
controlling for ducking when a person yells duck.

RM: It really doesn't even have to exist;-) But whether it does or not is an empirical question. But it's always good to be cautious about apparent S-R relationships between input variables (like changes in skin temperature and output variables (like shivering and sweating), especially when the output clearly has an effect on the input, as it does in this case.

BA: I don't find anything to disagree with there. But we shouldn't be ruling out other possibilities unless there is empirical evidence against them.

RM: In order to test these possibilities I think we have to have a
clear model of feedforward control. Then we can design a study that
tests whether feedforward control or (as I suspect) feedback control
of a higher level perception is what is actually going on in
situations that appear to involve feedforward control. Would you like
to work on designing such an experiment with me?

Best

Rick

···

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

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

[From Bruce Abbott (2014.01.28.1810 EST)]

Rick Marken (2014.01.28.1150)]

Bruce Abbott (2014.01.28.1115 EST)--

RM: [Feedforward] might not be a fact.

BA: Scenario 1: Someone yells "Rick, Duck!" You instantly dive for the

floor, just as a huge wrecking ball careens through your living room, barely
missing you. Result: you are still alive and well.

RM: I think you have to explain to me what feedforward control _is_.
Your scenario 1 doesn;t seem to involve feedforward control, at least as I
understand it. My understanding is that feedforward control involves the
system acting on it's own prediction of the future state of a disturbance to
a controlled variable in order to preemptively deal with the effects of that
disturbance. In Scenario 1 it's the person who yells "Duck" rather than,
Rick, the person controlling for not getting hit, who is doing the
predicting. The prediction will lead to preemptive action if, as you say,
Rick is controlling for ducking when someone yells "duck". SO all there is
is negative feedback control involved in this scenario as far as I can see;
the person who yells dick is controlling for Rick not getting hurt and Rick
is controlling for ducking when a person yells duck.

BA: I described Ashby's theoretically errorless system as one that senses
the disturbance and creates an opposing variable that mirrors the
disturbance, much like the output of a negative feedback control system
except that it cancels the disturbance effect exactly. However, feed
forward does not necessarily create such a mirror variable. Feed forward
means to use information about the disturbance that is available at least
slightly ahead of the disturbance's arrival at the variable to be protected
from the disturbance. In my Scenario 1, a warning to duck was given ahead of
the arrival of the impending disturbance to a number of Rick's controlled
variables, including perhaps his essential variables. By acting
appropriately (ducking), Rick could prevent the disturbance from having
those effects.

I admit that I didn't present the clearest example, but I believe that it
still embodies the essential features of feed forward control.

It is true that the warning was given by someone else, but all that really
matters is that, because of the warning, the impending disturbance was
detected in advance of its potential effects, and that Rick acted on the
warning in the appropriate way (by ducking). It is true that Rick would use
his own, negative feedback control systems to do the ducking, but that is
just the means through which the preventive action is taken in this example.
What is crucial for this example is that action is taken to oppose the
potential effects of the disturbance ahead of the actual event, and thus
prevent the disturbance from having those effects. A negative feedback
system would have to wait for the disturbance (in this case, the impact of
the wrecking ball) to generate an error in one or more control systems
before it initiated a counteraction.

Rick could have responded differently to the word "Duck!" He might have
thought that someone was calling him a duck and just stood there, feeling a
bit miffed, until the wrecking ball struck him down. But let's assume that
he took it as a warning that something was about to hit him, and ducked in
order to prevent that outcome. Advance warning, advance action to neutralize
the potential effects of the disturbance, effects of the disturbance
avoided. That's how feed forward control works.

RM: It really doesn't even have to exist;-) But whether it does or not is

an empirical question. But it's always good to be cautious about apparent
S-R relationships between input variables (like changes in skin temperature
and output variables (like shivering and sweating), especially when the
output clearly has an effect on the input, as it does in this case.

BA: I don't find anything to disagree with there. But we shouldn't be

ruling out other possibilities unless there is empirical evidence against
them.

RM: In order to test these possibilities I think we have to have a clear
model of feedforward control. Then we can design a study that tests whether
feedforward control or (as I suspect) feedback control of a higher level
perception is what is actually going on in situations that appear to involve
feedforward control. Would you like to work on designing such an experiment
with me?

I would prefer to test feed forward control in the form that Ashby
described, since that's what really started this thread. Do you have a
proposal?

Bruce

[From Rick Marken (2014.01.28.1620)]

Bruce Abbott (2014.01.28.1810 EST)--

RM: I think you have to explain to me what feedforward control _is_.

BA: I described Ashby's theoretically errorless system as one that senses
the disturbance and creates an opposing variable that mirrors the
disturbance, much like the output of a negative feedback control system
except that it cancels the disturbance effect exactly.

RM: In order to test these possibilities I think we have to have a clear
model of feedforward control. Then we can design a study that tests whether
feedforward control or (as I suspect) feedback control of a higher level
perception is what is actually going on in situations that appear to involve
feedforward control. Would you like to work on designing such an experiment
with me?

BA: I would prefer to test feed forward control in the form that Ashby
described, since that's what really started this thread. Do you have a
proposal?

Yes, how about writing an Ashby feed-forward model of the controlling
a person does in a tracking task. Then let's see how the model works
in various versions of the task (I'm thinking we could mainly vary the
nature of the disturbances), compare that to a PCT model of behavior
and then compare the behavior of the two models to that of humans in
the same situations.

Whaddaya think?

Best

Rick

···

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

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

[From Bruce Abbott (2014.01.28.2105 EST)

Rick Marken (2014.01.28.1620) --

Bruce Abbott (2014.01.28.1810 EST)

RM: I think you have to explain to me what feedforward control _is_.

BA: I described Ashby's theoretically errorless system as one that
senses the disturbance and creates an opposing variable that mirrors
the disturbance, much like the output of a negative feedback control
system except that it cancels the disturbance effect exactly.

RM: In order to test these possibilities I think we have to have a
clear model of feedforward control. Then we can design a study that
tests whether feedforward control or (as I suspect) feedback control
of a higher level perception is what is actually going on in
situations that appear to involve feedforward control. Would you like
to work on designing such an experiment with me?

BA: I would prefer to test feed forward control in the form that Ashby
described, since that's what really started this thread. Do you have a
proposal?

RM: Yes, how about writing an Ashby feed-forward model of the controlling a
person does in a tracking task. Then let's see how the model works in
various versions of the task (I'm thinking we could mainly vary the nature
of the disturbances), compare that to a PCT model of behavior and then
compare the behavior of the two models to that of humans in the same
situations.

RM: Whaddaya think?

BA: What do you mean by "vary the nature of the disturbances"? The Achilles
heel of feed forward control is that it can compensate only for disturbances
it can sense. Feedback systems don't have this problem, because they work
to compensate deviations of input from reference, no matter what their
source. For example, a feed forward cruise control might be built to sense
the force of the wind against the frontal area of the car and compensate for
the wind's effect on the car's speed through appropriate adjustments of the
engine throttle setting. But such a system would fail to compensate for the
effect on speed due to gravitational pull as the car ascends and descends
hills. If by "vary the nature of the disturbances" you mean to introduce
other disturbances in addition to the one (or ones) that the feed forward
system senses, then clearly the feed forward system will not be able to
compensate for them. (I made no claim that feed forward systems are in all
cases superior to feedback systems, only that they can be under certain
conditions.)

BA: We already know that humans doing the standard tracking task usually do
an excellent job of controlling, especially after they've had some practice
at it. It's a situation where feed forward control could not offer much of
an advantage. However, if you simply want proof that such a system is
theoretically capable of controlling with zero error against a known
disturbance, then we could use the tracking task to demonstrate that. As for
comparing the behavior of the feed forward and feedback models against a
person's behavior in the tracking task, the person doing the tracking task
has no access (advance or otherwise) to the disturbance, and so obviously
must be using negative feedback control in that task.

So the question is, in what sorts of tasks might feed forward control
present a significant advantage over feedback control?

Bruce

[From Rick Marken (2014.01.28.1825)]

Bruce Abbott (2014.01.28.2105 EST)--

BA: We already know that humans doing the standard tracking task usually do
an excellent job of controlling, especially after they've had some practice
at it. It's a situation where feed forward control could not offer much of
an advantage.

RM: I was thinking of a pursuit tracking task, where the disturbance
(target movement) is quite visible. That's a situation where it is
known that tracking a predictable disturbance (like a sine wave) can
be quite a bit better than tracking an unpredictable one (other things
being as equal as you can make them). It looks like feedforward might
be involved there, right?

Best

Rick

However, if you simply want proof that such a system is

···

theoretically capable of controlling with zero error against a known
disturbance, then we could use the tracking task to demonstrate that. As for
comparing the behavior of the feed forward and feedback models against a
person's behavior in the tracking task, the person doing the tracking task
has no access (advance or otherwise) to the disturbance, and so obviously
must be using negative feedback control in that task.

So the question is, in what sorts of tasks might feed forward control
present a significant advantage over feedback control?

Bruce

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

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

So the question is, in what sorts of tasks might feed forward control present a significant advantage over feedback control?

Fact: You can’t tell what a person is doing by looking at them.

What if, for some reason, you already had this information. My intuition tells me that feed-forward control would occur if somebody was given knowledge or information that would be “impossible” to have immediately, without a sufficiently long period of observation and “testing”.

Case study: somebody tells you to duck as a cannonball approaches the space occupied by your head. You could have seen this immediately, had you been looking, but you weren’t looking. You duck, thus preventing your death with apriori knowledge.

But that’s a weird example. Here’s an even weirder one:

You want to steal from a tribe of natives which you’ve been given secret information about regarding their rituals. One of these rituals involves the placing of valuable artifacts in a cave. You want to steal these artifacts. You would not be able to know the location of these artifacts unless you were told by a member of the tribe. You could not get this information otherwise, but now that you know, you could steal the goods.

Feed-forward and feed-back work together. Feed forward seems to involve perceptions which are not built up from first order systems. The information about the location of the cave or the rituals, or about the meaning of communicated order to duck are fed directly to the appropriate level and the information does not serve to control a perception directly, but can be combined with perceptual control to do cool things, such as live and get rich.

I think feed forward has to do with symbolic information whereas feed back has to do with actual behavior.

Please don’t be afraid to use extremely fabricated examples like I do. It generally helps with the creative process. We need to figure out this whole feed-forward thing because it has a lot to do with a lot of advanced applications.

Who here knows a lot about immunology? I believe the immune system is a better system to study to compare and contrast feed forward and feed back control. Thermoregulation is too simple a process it seems to require feed forward processes. But the adaptive immune system control over the innate immune system is a gem to study to look for this.

···

On Tuesday, January 28, 2014, Bruce Abbott bbabbott@frontier.com wrote:

[From Bruce Abbott (2014.01.28.2105 EST)

Rick Marken (2014.01.28.1620) –

Bruce Abbott (2014.01.28.1810 EST)

RM: I think you have to explain to me what feedforward control is.

BA: I described Ashby’s theoretically errorless system as one that

senses the disturbance and creates an opposing variable that mirrors

the disturbance, much like the output of a negative feedback control

system except that it cancels the disturbance effect exactly.

RM: In order to test these possibilities I think we have to have a

clear model of feedforward control. Then we can design a study that

tests whether feedforward control or (as I suspect) feedback control

of a higher level perception is what is actually going on in

situations that appear to involve feedforward control. Would you like

to work on designing such an experiment with me?

BA: I would prefer to test feed forward control in the form that Ashby

described, since that’s what really started this thread. Do you have a

proposal?

RM: Yes, how about writing an Ashby feed-forward model of the controlling a

person does in a tracking task. Then let’s see how the model works in

various versions of the task (I’m thinking we could mainly vary the nature

of the disturbances), compare that to a PCT model of behavior and then

compare the behavior of the two models to that of humans in the same

situations.

RM: Whaddaya think?

BA: What do you mean by “vary the nature of the disturbances”? The Achilles

heel of feed forward control is that it can compensate only for disturbances

it can sense. Feedback systems don’t have this problem, because they work

to compensate deviations of input from reference, no matter what their

source. For example, a feed forward cruise control might be built to sense

the force of the wind against the frontal area of the car and compensate for

the wind’s effect on the car’s speed through appropriate adjustments of the

engine throttle setting. But such a system would fail to compensate for the

effect on speed due to gravitational pull as the car ascends and descends

hills. If by “vary the nature of the disturbances” you mean to introduce

other disturbances in addition to the one (or ones) that the feed forward

system senses, then clearly the feed forward system will not be able to

compensate for them. (I made no claim that feed forward systems are in all

cases superior to feedback systems, only that they can be under certain

conditions.)

BA: We already know that humans doing the standard tracking task usually do

an excellent job of controlling, especially after they’ve had some practice

at it. It’s a situation where feed forward control could not offer much of

an advantage. However, if you simply want proof that such a system is

theoretically capable of controlling with zero error against a known

disturbance, then we could use the tracking task to demonstrate that. As for

comparing the behavior of the feed forward and feedback models against a

person’s behavior in the tracking task, the person doing the tracking task

has no access (advance or otherwise) to the disturbance, and so obviously

must be using negative feedback control in that task.

So the question is, in what sorts of tasks might feed forward control

present a significant advantage over feedback control?

Bruce

[From Richard Pfau (2014.01.28 23:36 EST)]

`Ref: [From Rick Marken (2014.01.28.1620)]

RM: Yes, how about writing an Ashby feed-forward model of the controlling a person does in a tracking task. Then let’s see how the model works in various versions of the task … Whaddaya think?

RP: Some feedback: Your suggestion for using a tracking task to test models of feed-forward and PCT feedback is highly biased in that the PCT model would surely perform better in such a tracking task.`


`And a thought:  In some cases, the phenomena called feedforward does seem to be taking place.  (a) Bruce's example of the wrecking ball seems like one.  (b) Another seems to be when you put on a coat when it's cold outside (i.e., before you are physically cold) to prevent yourself from becoming chilled when you go outside.`

``

`However, if as you indicated (2012.08.21.0915) and Bill Powers seemed to concur (2012.08.21.1217 MDT) that you "understood 'feedforward' to be basically equivalent to what we call a reference signal" so that [here's my interpretation now of what you seem to mean] (a) when someone shouts "Duck!" then  reference signals (at the relationship and program levels?) are such that "such shouting (i.e., 'Duck!') indicates a relationship with danger" and should be quickly acted on, whereas (b)reference signals (again at the relationship and program levels?)for "when the weather is perceived to be cold, you should put on warm warm clothing when you go outside" results in the output of putting on a coat before going outside.  `

``

`Are such interpretations more or less compatable with your thinking -- such that you feel that apparent feedforward phenomena are already covered by the PCT model?`

``

`With Regards,`

`Richard Pfau`

Oh, we are back here again! What a live topic! Anyone think this makes sense as an answer? I originally wrote it as a proposal for a journal article that was rejected and I need to write the full article at some point!

Predictive (or feedforward) theories of motor control have replaced simple feedback theories. Yet

the limitations of feedforward theories have been recognized and „hybrid‟ models have attempted

to reintegrate feedback, but within a secondary role. We critique hybrid models and propose that

the field would progress by „going full circle‟ to make feedback control primary. Sensory feedback

is critical because it corrects inaccurate predictions. One contemporary feedback theory

(Perceptual Control Theory; PCT) can manage delays in neural signaling, circumvent limits in

sensory feedback, and learn to optimize control. We identify four original components of PCT as

alternative ways of implementing feedforward processes to enhance feedback control over four

different, parallel timescales. We discuss the implications for a unified understanding of control.

‘Going Full Circle’: Is Control More Important Than Prediction? Warren Mansell 7/2/12

My article proposes that the „hybrid‟ models of control overemphasize prediction at the expense of

control and should be superseded by returning „full circle‟ to a feedback theory of control that

subsumes feedforward processes within its architecture.

I plan to start the article with accessible examples. At the turn of the 20th century, John Dewey

proposed that “the motor response determines the stimulus just as truly as the sensory stimulus

determines movement” - while our environment can trigger changes in our behavior, it is also the

case that our behavior is affecting the environment and we sense these effects as ‘feedback’. Take

the example of chasing a moving target during hunting – a vital skill for any predator’s survival.

Processing current sensory feedback is critical to control. The prey may change direction at any

moment, as may the angle and terrain of the ground beneath the predator’s feet. Moreover, as the

predator shifts its speed and angle to compensate, the visual image of the prey it perceives on its

retina will change instantly. The brain of the predator must process current sensory and motor

information in such a way that it regularly, and efficiently, reaches its target – or ultimately it dies.

Early 20th century engineers, recognizing the importance of feedback control, developed

technologies we take for granted today, such as thermostats, amplifiers, flight control systems, and

industrial and medical devices - some of which have an accuracy of up to four parts per million.

Within psychology, feedback models peaked with the development of ‘cybernetics’ (Ashby, 1952;

Wiener, 1948). Within a few decades, however, the pendulum had swung towards the

development of feedforward theories of control (Oosting & Dickerson, 1987). Feedforward theories

are varied, yet each computes the required actions for control based on either ongoing motor

signals or computational models that predict in advance states of the body and the environment.

Despite many decades of this research, the need for considering feedback did not go away. For

example, Optimal Control Theory was converted to a ‘hybrid’ - Optimal Feedback Control Theory –

to improve its match with observed data (Todorov & Jordan, 2002). Indeed, there are examples

across wide domains where models utilizing feedforward control rely critically on feedback

processing for accurate performance (Perkell, in press; Saunders & Vijayakumar, 2011).

The limitations of recent hybrid models are increasingly well documented, including their inability to

learn their control parameters, to engage in hierarchical control, and to control cognition

(Diedrichsen et al., 2010). Consequently, there have been recent calls for a more unified,

parsimonious theory of motor control that is directly testable (Arjemian & Hogan, 2010).

In this article, we heed this call and review evidence for a theory of feedback control that has the

capacity to restore confidence in the field: Perceptual Control Theory (PCT; Powers, Clark &

McFarland, 1960a,1960b; Powers, 1973, 2008). We make explicit four components of PCT

subsumed within its architecture that ‘feedforward’ their effects in parallel over four different time

scales (leaky integration of efferent signals, hierarchies, reorganization learning, and selfgenerated

feedback).

The article will initially critique five reasons commonly offered for the apparent advantages of

feedforward control within current „hybrid‟ models:

A. Signal delays make feedback control ineffective. This view seems inconsistent with evidence of

the performance advantages of sensory feedback during early stages of control, and for fast

movements (Boer et al., 2011; Tunik et al., 2009). Moreover, when disturbances are not

predictable, a model based on feedback processing can merely reduce its performance to the

same as that of a feedforward system. A feedback model with nerve signal delays performs

effectively over a range of simulated frequencies (Powers, 2008).

B. Sensory feedback is often unavailable. Under conditions of limited sensory feedback (e.g.

blocked vision), the brain seems capable of sensory substitution, whereby alternative streams

of sensory feedback, such as proprioception, are used (Merabet & Pascual-Leone, 2010).

C. Feedforward motor signals improve control. There are examples of feedforward motor signals,

such as the vestibulo-ocular reflex. Yet, these require stabilization through feedback processing

(Montfoort et al., 2008). The predicted sensory consequences of one’s own actions do,

however, appear to be used across a range of tasks; thus, a feedback theory needs to account

for this.

D. Control parameters for feedback systems need to be learned. Learning enhances control, yet

evidence shows that sensory feedback is typically required for this learning (Perkell, in press).

E. Internal models can predict the correct motor signal. The kinematic properties of the body and

environment are associated with specific brain regions (Grafton et al., 2008). Yet, internal

models can be complex and inaccurate (e.g. Cloete & Wallis, 2009). Thus, a parsimonious

account of internal model generation constantly updated by sensory feedback is desirable.

At this point within the article, accessible pop-out boxes will be used to explain PCT and the

evidence for the accuracy of models of behavioral tasks (Marken, 2009) and its explanation for

observed behavior (e.g. Pellis & Bell, 2011). In summary, the reference values for a feedback unit

in PCT are set by the efferent signals of a hierarchically superordinate feedback unit that operates

over a longer timescale of perceptual input. The whole system „controls perception‟, within motor

and environmental constraints. The lowest system of this hierarchy is the tendon reflex. An afferent

signal representing muscle tension is compared to an efferent reference signal entering the spinal

motor neuron, with the difference – error – being amplified by the muscle and converted to an

output force to maintain a regulated tension (Powers, 1973).

Importantly, there are four original features of PCT that could be conceptualized as ‘feed-forward’

and subserve its feedback architecture (Powers et al., 1960; Powers, 1973).

A pop-out box will

provide equations of these components and their location in a diagram. They are as follows:

(1) Leaky integration: efferent signals are integrated over periods of several milliseconds.

Therefore, the strength of a response to a disturbance in a constant direction will increase over

very brief periods. This counters signal delays (A) described above.

(2) Hierarchies: higher-level systems perceive changes that unfold over fractions of a second or

greater, and their efferent signals set the reference values for lower-level systems; these are

equivalent to the expected sensory consequences of action described above ©. For example, a

higher-level system for the desired angular position of a joint sets the desired velocity of the joint

for the system below. A mid-level system controls this variable via the efferent signals that it, in

turn, sends down to regulate acceleration via muscular forces at a lower level. Hierarchical PCT

models accurately match observed data (Marken, 1986; Powers, 2008). Future research could test

if a PCT model can utilize this information for a range of published tasks.

(3) Reorganization learning: during repeated or ongoing situations, the control parameters are

optimized through a trial-and-error learning process known as „reorganization‟, such that their

values are fed forward for improved performance. This addresses issue (D) described above.

Alternative streams of afferent input can develop through reorganization, addressing point (B).

Powers (2008) constructed and tested a computer model of 14-joint arm movement that learned its

optimal control parameters through this algorithm. Future research could test further models.

(4) Self-generated feedback: In PCT, when sensory feedback is unavailable and sensory

substitution has limited effectiveness, and/or when action is prevented, the control system can

enter ‘imagination mode’ (Powers, 1973). In this state, the reference values can be short-circuited

internally to the organism, so that approximate states of the self and world can be modeled ‘as if’

they are occurring in the environment. The motor plan that is modeled as most successful can then

be fed forward when the opportunity to control the environment is next available, subject to online

alterations through feedback control. This addresses point (E) above. Convergent evidence is

consistent with this process. Hierarchically organized closed loops of biafferent neural signals are

integral to the brain (Strick et al., 2011). Sensory deprivation in humans does indeed result in the

formation of internally generated perceptions such as spontaneous visual imagery (Mason &

Brady, 2009). Sensory deprivation in simulated robots leads to the emergence of spontaneous

internal generation of perception via a closed loop process (Gigliotta et al., 2010), and a study of

robotic systems utilizing sensory feedback models based on PCT outperformed competing models

by up to 95% (Rabinovich & Jennings, 2010).

A summary of several further advantages of adopting a PCT model of control will follow in the

article. First, a sense of control is recognised as an intrinsic need (Leotti et al., 2010), and

therefore it is appropriate that PCT places present-moment control, rather than prediction per se,

at its core. Second, cognitive control can be embodied within a PCT model (Mansell, 2011). Third,

the parsimony of PCT has made it highly adaptable to a variety of disciplines, e.g. mental health

(Carey, 2011) and organisational psychology (Vancouver & Scherbaum, 2008). Finally, the

limitations of PCT and directions for future research will be described.

Recent References

Ajemian, R., & Hogan, N. (2010). Experimenting with theoretical motor neuroscience. Journal of Motor

Behavior, 42, 333-342.

Carey, T. A. (2011). Exposure and reorganization: The what and how of effective psychotherapy. Clinical

Psychology Review, 31, 236-248.

Cloete, S. R., & Wallis, G. (2009). Limitations of feedforward control in multiple-phase steering movements.

Experimental Brain Research, 195, 481-487.

Diedrichsen, J., Shadmehr, R., & Ivry, R. B. (2010). The coordination of movement: optimal feedback control

and beyond. Trends in Cognitive Sciences, 14, 31-39.

Gigliotta, O., Pezzulo, G., & Nolfi, S. (2010). Emergences of an internal model in evolving robots subjected to

sensory deprivation. Lecture Notes in Computer Science, 6226, 575-586.

Grafton, S. T., Schmidt, P., Van Horn, J., & Diedrichsen, J. (2008). Neural substrates of visuomotor learning

based on improved feedback control and prediction. Neuroimage, 39, 1383-1395.

Leotti, L. A., Iyengar, S. S., & Ochsner, K. N. (2010). Born to choose: the origins and value of the need for

control. Trends in Cognitive Sciences, 14, 457-463.

Mansell, W. (2011). Control of perception should be operationalised as a fundamental property of the

nervous system. Topics in Cognitive Science, 3, 257-261.

Merabet, L. B., & Pascual-Leone, A. (2010). Neural reorganization following sensory loss: The opportunity of

change. Nature Reviews Neuroscience, 11, 44-52.

Marken, R. S. (2009) You say you had a revolution: Methodological foundations of closed-loop psychology.

Review of General Psychology, 13, 137-145.

Mason, O. J., & Brady, F. (2009). The psychotomimetic effects of short-term sensory deprivation. Journal of

Nervous and Mental Disease, 197, 783-785.

Montfoort, I., Van Der Geest, J. N., Slijper, H. P., De Zeeuw, C. I., & Frens, M. A. (2008). Adaptation of the

cervico- and vestibulo-ocular reflex in whiplash injury patients. Journal of Neurotrauma, 25, 687-693.

Tunik, E., Houk, J. C., & Grafton, S. T. (2009). Basal ganglia contribution to the initiation of corrective

submovements. Neuroimage, 47, 1757-1766.

Pellis, S., & Bell, H. (2011). Closing the circle between perceptions and behavior: A cybernetic view of

behavior and its consequences for studying motivation and development. Developmental Cognitive

Neuroscience, 1, 404-413.

Perkell, J. S. (in press). Movement goals and feedback and feedforward control mechanisms in speech

production. Journal of Neurolinguistics.

Saunders, I., & Vijayakumar, S. (2011). The role of feed-forward and feedback processes for closed-loop

prosthesis control. Journal of Neuroengineering and Rehabilitation, 8, 60.

Powers, W. T. (2008). Living Control Systems III: The Fact of Control. Benchmark Publications.

Rabinovich, Z., & Jennings, N. R. (2010). A hybrid controller based on the egocentric perceptual principle.

Robotics and Autonomous Systems, 58, 1039-1048.

Strick, P. L., Dum, R. P., Fiez, J. A. (2011). Cerebellum and Nonmotor Function. The Annual Review of

Neuroscience, 32, 413-434.

Vancouver, J. B. & Scherbaum, C. A. (2008). Do we self-regulate actions or perceptions? A test of two

computational models. Computational and Mathematical Organizational Theory, 14, 1-22.

Earlier References

Ashby, W. R. (1952). A Design for a Brain. Chapman & Hall.

Oosting, K. W. & Dickerson, S. L. (1987). Feed forward control for stabilisation. ASME

Powers, W. T. (1973). Behavior: The Control of Perception. Chicago, IL: Aldine.

Powers, W. T., Clark, R. K., & McFarland, R. L. (1960a). A general feedback theory of human behavior. Part

I. Perceptual and Motor Skills, 11, 71-88.

Powers, W. T., Clark, R. K., & McFarland, R. L. (1960). A general feedback theory of human behavior. Part

II. Perceptual and Motor Skills, 11, 309-323.

Todorov, E., & Jordan, M. I. (2002). Optimal feedback control as a theory of motor coordination. Nature

Neuroscience, 5, 1226-1235.

Wiener, N. (1948). Cybernetics: Or Control and Communication in the Animal and the Machine. MIT Press.

···

On Wed, Jan 29, 2014 at 4:35 AM, Richard H. Pfau richardpfau4153@aol.com wrote:

[From Richard Pfau (2014.01.28 23:36 EST)]

`Ref: [From Rick Marken (2014.01.28.1620)]

RM: Yes, how about writing an Ashby feed-forward model of the controlling a person does in a tracking task. Then let’s see how the model works in various versions of the task … Whaddaya think?

RP: Some feedback: Your suggestion for using a tracking task to test models of feed-forward and PCT feedback is highly biased in that the PCT model would surely perform better in such a tracking task.`

And a thought: In some cases, the phenomena called feedforward does seem to be taking place. (a) Bruce's example of the wrecking ball seems like one. (b) Another seems to be when you put on a coat when it's cold outside (i.e., before you are physically cold) to prevent yourself from becoming chilled when you go outside.

``

However, if as you indicated (2012.08.21.0915) and Bill Powers seemed to concur (2012.08.21.1217 MDT) that you "understood 'feedforward' to be basically equivalent to what we call a reference signal" so that [here's my interpretation now of what you seem to mean] (a) when someone shouts "Duck!" then reference signals (at the relationship and program levels?) are such that "such shouting (i.e., 'Duck!') indicates a relationship with danger" and should be quickly acted on, whereas (b)reference signals (again at the relationship and program levels?)for "when the weather is perceived to be cold, you should put on warm warm clothing when you go outside" results in the output of putting on a coat before going outside.

``

Are such interpretations more or less compatable with your thinking -- such that you feel that apparent feedforward phenomena are already covered by the PCT model?

``

With Regards,

Richard Pfau


Dr Warren Mansell
Reader in Psychology
Cognitive Behavioural Therapist & Chartered Clinical Psychologist
School of Psychological Sciences

Coupland I
University of Manchester
Oxford Road
Manchester M13 9PL
Email: warren.mansell@manchester.ac.uk

Tel: +44 (0) 161 275 8589

Website: http://www.psych-sci.manchester.ac.uk/staff/131406

See teamstrial.net for further information on our trial of CBT for Bipolar Disorders in NW England

The highly acclaimed therapy manual on A Transdiagnostic Approach to CBT using Method of Levels is available now.

Check www.pctweb.org for further information on Perceptual Control Theory

I would respectfully ask to be removed from all e-mail within this group at this time.

···

On Wed, Jan 29, 2014 at 9:29 AM, Warren Mansell wmansell@gmail.com wrote:

Oh, we are back here again! What a live topic! Anyone think this makes sense as an answer? I originally wrote it as a proposal for a journal article that was rejected and I need to write the full article at some point!

Predictive (or feedforward) theories of motor control have replaced simple feedback theories. Yet

the limitations of feedforward theories have been recognized and „hybrid‟ models have attempted

to reintegrate feedback, but within a secondary role. We critique hybrid models and propose that

the field would progress by „going full circle‟ to make feedback control primary. Sensory feedback

is critical because it corrects inaccurate predictions. One contemporary feedback theory

(Perceptual Control Theory; PCT) can manage delays in neural signaling, circumvent limits in

sensory feedback, and learn to optimize control. We identify four original components of PCT as

alternative ways of implementing feedforward processes to enhance feedback control over four

different, parallel timescales. We discuss the implications for a unified understanding of control.

‘Going Full Circle’: Is Control More Important Than Prediction? Warren Mansell 7/2/12

My article proposes that the „hybrid‟ models of control overemphasize prediction at the expense of

control and should be superseded by returning „full circle‟ to a feedback theory of control that

subsumes feedforward processes within its architecture.

I plan to start the article with accessible examples. At the turn of the 20th century, John Dewey

proposed that “the motor response determines the stimulus just as truly as the sensory stimulus

determines movement” - while our environment can trigger changes in our behavior, it is also the

case that our behavior is affecting the environment and we sense these effects as ‘feedback’. Take

the example of chasing a moving target during hunting – a vital skill for any predator’s survival.

Processing current sensory feedback is critical to control. The prey may change direction at any

moment, as may the angle and terrain of the ground beneath the predator’s feet. Moreover, as the

predator shifts its speed and angle to compensate, the visual image of the prey it perceives on its

retina will change instantly. The brain of the predator must process current sensory and motor

information in such a way that it regularly, and efficiently, reaches its target – or ultimately it dies.

Early 20th century engineers, recognizing the importance of feedback control, developed

technologies we take for granted today, such as thermostats, amplifiers, flight control systems, and

industrial and medical devices - some of which have an accuracy of up to four parts per million.

Within psychology, feedback models peaked with the development of ‘cybernetics’ (Ashby, 1952;

Wiener, 1948). Within a few decades, however, the pendulum had swung towards the

development of feedforward theories of control (Oosting & Dickerson, 1987). Feedforward theories

are varied, yet each computes the required actions for control based on either ongoing motor

signals or computational models that predict in advance states of the body and the environment.

Despite many decades of this research, the need for considering feedback did not go away. For

example, Optimal Control Theory was converted to a ‘hybrid’ - Optimal Feedback Control Theory –

to improve its match with observed data (Todorov & Jordan, 2002). Indeed, there are examples

across wide domains where models utilizing feedforward control rely critically on feedback

processing for accurate performance (Perkell, in press; Saunders & Vijayakumar, 2011).

The limitations of recent hybrid models are increasingly well documented, including their inability to

learn their control parameters, to engage in hierarchical control, and to control cognition

(Diedrichsen et al., 2010). Consequently, there have been recent calls for a more unified,

parsimonious theory of motor control that is directly testable (Arjemian & Hogan, 2010).

In this article, we heed this call and review evidence for a theory of feedback control that has the

capacity to restore confidence in the field: Perceptual Control Theory (PCT; Powers, Clark &

McFarland, 1960a,1960b; Powers, 1973, 2008). We make explicit four components of PCT

subsumed within its architecture that ‘feedforward’ their effects in parallel over four different time

scales (leaky integration of efferent signals, hierarchies, reorganization learning, and selfgenerated

feedback).

The article will initially critique five reasons commonly offered for the apparent advantages of

feedforward control within current „hybrid‟ models:

A. Signal delays make feedback control ineffective. This view seems inconsistent with evidence of

the performance advantages of sensory feedback during early stages of control, and for fast

movements (Boer et al., 2011; Tunik et al., 2009). Moreover, when disturbances are not

predictable, a model based on feedback processing can merely reduce its performance to the

same as that of a feedforward system. A feedback model with nerve signal delays performs

effectively over a range of simulated frequencies (Powers, 2008).

B. Sensory feedback is often unavailable. Under conditions of limited sensory feedback (e.g.

blocked vision), the brain seems capable of sensory substitution, whereby alternative streams

of sensory feedback, such as proprioception, are used (Merabet & Pascual-Leone, 2010).

C. Feedforward motor signals improve control. There are examples of feedforward motor signals,

such as the vestibulo-ocular reflex. Yet, these require stabilization through feedback processing

(Montfoort et al., 2008). The predicted sensory consequences of one’s own actions do,

however, appear to be used across a range of tasks; thus, a feedback theory needs to account

for this.

D. Control parameters for feedback systems need to be learned. Learning enhances control, yet

evidence shows that sensory feedback is typically required for this learning (Perkell, in press).

E. Internal models can predict the correct motor signal. The kinematic properties of the body and

environment are associated with specific brain regions (Grafton et al., 2008). Yet, internal

models can be complex and inaccurate (e.g. Cloete & Wallis, 2009). Thus, a parsimonious

account of internal model generation constantly updated by sensory feedback is desirable.

At this point within the article, accessible pop-out boxes will be used to explain PCT and the

evidence for the accuracy of models of behavioral tasks (Marken, 2009) and its explanation for

observed behavior (e.g. Pellis & Bell, 2011). In summary, the reference values for a feedback unit

in PCT are set by the efferent signals of a hierarchically superordinate feedback unit that operates

over a longer timescale of perceptual input. The whole system „controls perception‟, within motor

and environmental constraints. The lowest system of this hierarchy is the tendon reflex. An afferent

signal representing muscle tension is compared to an efferent reference signal entering the spinal

motor neuron, with the difference – error – being amplified by the muscle and converted to an

output force to maintain a regulated tension (Powers, 1973).

Importantly, there are four original features of PCT that could be conceptualized as ‘feed-forward’

and subserve its feedback architecture (Powers et al., 1960; Powers, 1973).

A pop-out box will

provide equations of these components and their location in a diagram. They are as follows:

(1) Leaky integration: efferent signals are integrated over periods of several milliseconds.

Therefore, the strength of a response to a disturbance in a constant direction will increase over

very brief periods. This counters signal delays (A) described above.

(2) Hierarchies: higher-level systems perceive changes that unfold over fractions of a second or

greater, and their efferent signals set the reference values for lower-level systems; these are

equivalent to the expected sensory consequences of action described above ©. For example, a

higher-level system for the desired angular position of a joint sets the desired velocity of the joint

for the system below. A mid-level system controls this variable via the efferent signals that it, in

turn, sends down to regulate acceleration via muscular forces at a lower level. Hierarchical PCT

models accurately match observed data (Marken, 1986; Powers, 2008). Future research could test

if a PCT model can utilize this information for a range of published tasks.

(3) Reorganization learning: during repeated or ongoing situations, the control parameters are

optimized through a trial-and-error learning process known as „reorganization‟, such that their

values are fed forward for improved performance. This addresses issue (D) described above.

Alternative streams of afferent input can develop through reorganization, addressing point (B).

Powers (2008) constructed and tested a computer model of 14-joint arm movement that learned its

optimal control parameters through this algorithm. Future research could test further models.

(4) Self-generated feedback: In PCT, when sensory feedback is unavailable and sensory

substitution has limited effectiveness, and/or when action is prevented, the control system can

enter ‘imagination mode’ (Powers, 1973). In this state, the reference values can be short-circuited

internally to the organism, so that approximate states of the self and world can be modeled ‘as if’

they are occurring in the environment. The motor plan that is modeled as most successful can then

be fed forward when the opportunity to control the environment is next available, subject to online

alterations through feedback control. This addresses point (E) above. Convergent evidence is

consistent with this process. Hierarchically organized closed loops of biafferent neural signals are

integral to the brain (Strick et al., 2011). Sensory deprivation in humans does indeed result in the

formation of internally generated perceptions such as spontaneous visual imagery (Mason &

Brady, 2009). Sensory deprivation in simulated robots leads to the emergence of spontaneous

internal generation of perception via a closed loop process (Gigliotta et al., 2010), and a study of

robotic systems utilizing sensory feedback models based on PCT outperformed competing models

by up to 95% (Rabinovich & Jennings, 2010).

A summary of several further advantages of adopting a PCT model of control will follow in the

article. First, a sense of control is recognised as an intrinsic need (Leotti et al., 2010), and

therefore it is appropriate that PCT places present-moment control, rather than prediction per se,

at its core. Second, cognitive control can be embodied within a PCT model (Mansell, 2011). Third,

the parsimony of PCT has made it highly adaptable to a variety of disciplines, e.g. mental health

(Carey, 2011) and organisational psychology (Vancouver & Scherbaum, 2008). Finally, the

limitations of PCT and directions for future research will be described.

Recent References

Ajemian, R., & Hogan, N. (2010). Experimenting with theoretical motor neuroscience. Journal of Motor

Behavior, 42, 333-342.

Carey, T. A. (2011). Exposure and reorganization: The what and how of effective psychotherapy. Clinical

Psychology Review, 31, 236-248.

Cloete, S. R., & Wallis, G. (2009). Limitations of feedforward control in multiple-phase steering movements.

Experimental Brain Research, 195, 481-487.

Diedrichsen, J., Shadmehr, R., & Ivry, R. B. (2010). The coordination of movement: optimal feedback control

and beyond. Trends in Cognitive Sciences, 14, 31-39.

Gigliotta, O., Pezzulo, G., & Nolfi, S. (2010). Emergences of an internal model in evolving robots subjected to

sensory deprivation. Lecture Notes in Computer Science, 6226, 575-586.

Grafton, S. T., Schmidt, P., Van Horn, J., & Diedrichsen, J. (2008). Neural substrates of visuomotor learning

based on improved feedback control and prediction. Neuroimage, 39, 1383-1395.

Leotti, L. A., Iyengar, S. S., & Ochsner, K. N. (2010). Born to choose: the origins and value of the need for

control. Trends in Cognitive Sciences, 14, 457-463.

Mansell, W. (2011). Control of perception should be operationalised as a fundamental property of the

nervous system. Topics in Cognitive Science, 3, 257-261.

Merabet, L. B., & Pascual-Leone, A. (2010). Neural reorganization following sensory loss: The opportunity of

change. Nature Reviews Neuroscience, 11, 44-52.

Marken, R. S. (2009) You say you had a revolution: Methodological foundations of closed-loop psychology.

Review of General Psychology, 13, 137-145.

Mason, O. J., & Brady, F. (2009). The psychotomimetic effects of short-term sensory deprivation. Journal of

Nervous and Mental Disease, 197, 783-785.

Montfoort, I., Van Der Geest, J. N., Slijper, H. P., De Zeeuw, C. I., & Frens, M. A. (2008). Adaptation of the

cervico- and vestibulo-ocular reflex in whiplash injury patients. Journal of Neurotrauma, 25, 687-693.

Tunik, E., Houk, J. C., & Grafton, S. T. (2009). Basal ganglia contribution to the initiation of corrective

submovements. Neuroimage, 47, 1757-1766.

Pellis, S., & Bell, H. (2011). Closing the circle between perceptions and behavior: A cybernetic view of

behavior and its consequences for studying motivation and development. Developmental Cognitive

Neuroscience, 1, 404-413.

Perkell, J. S. (in press). Movement goals and feedback and feedforward control mechanisms in speech

production. Journal of Neurolinguistics.

Saunders, I., & Vijayakumar, S. (2011). The role of feed-forward and feedback processes for closed-loop

prosthesis control. Journal of Neuroengineering and Rehabilitation, 8, 60.

Powers, W. T. (2008). Living Control Systems III: The Fact of Control. Benchmark Publications.

Rabinovich, Z., & Jennings, N. R. (2010). A hybrid controller based on the egocentric perceptual principle.

Robotics and Autonomous Systems, 58, 1039-1048.

Strick, P. L., Dum, R. P., Fiez, J. A. (2011). Cerebellum and Nonmotor Function. The Annual Review of

Neuroscience, 32, 413-434.

Vancouver, J. B. & Scherbaum, C. A. (2008). Do we self-regulate actions or perceptions? A test of two

computational models. Computational and Mathematical Organizational Theory, 14, 1-22.

Earlier References

Ashby, W. R. (1952). A Design for a Brain. Chapman & Hall.

Oosting, K. W. & Dickerson, S. L. (1987). Feed forward control for stabilisation. ASME

Powers, W. T. (1973). Behavior: The Control of Perception. Chicago, IL: Aldine.

Powers, W. T., Clark, R. K., & McFarland, R. L. (1960a). A general feedback theory of human behavior. Part

I. Perceptual and Motor Skills, 11, 71-88.

Powers, W. T., Clark, R. K., & McFarland, R. L. (1960). A general feedback theory of human behavior. Part

II. Perceptual and Motor Skills, 11, 309-323.

Todorov, E., & Jordan, M. I. (2002). Optimal feedback control as a theory of motor coordination. Nature

Neuroscience, 5, 1226-1235.

Wiener, N. (1948). Cybernetics: Or Control and Communication in the Animal and the Machine. MIT Press.


Kerry Patton
Author, CONTRACTED: America’s Secret Warriors
www.kerry-patton.com
(570)278-3618

On Wed, Jan 29, 2014 at 4:35 AM, Richard H. Pfau richardpfau4153@aol.com wrote:

[From Richard Pfau (2014.01.28 23:36 EST)]

`Ref: [From Rick Marken (2014.01.28.1620)]

RM: Yes, how about writing an Ashby feed-forward model of the controlling a person does in a tracking task. Then let’s see how the model works in various versions of the task … Whaddaya think?

RP: Some feedback: Your suggestion for using a tracking task to test models of feed-forward and PCT feedback is highly biased in that the PCT model would surely perform better in such a tracking task.`

And a thought: In some cases, the phenomena called feedforward does seem to be taking place. (a) Bruce's example of the wrecking ball seems like one. (b) Another seems to be when you put on a coat when it's cold outside (i.e., before you are physically cold) to prevent yourself from becoming chilled when you go outside.

``

However, if as you indicated (2012.08.21.0915) and Bill Powers seemed to concur (2012.08.21.1217 MDT) that you "understood 'feedforward' to be basically equivalent to what we call a reference signal" so that [here's my interpretation now of what you seem to mean] (a) when someone shouts "Duck!" then reference signals (at the relationship and program levels?) are such that "such shouting (i.e., 'Duck!') indicates a relationship with danger" and should be quickly acted on, whereas (b)reference signals (again at the relationship and program levels?)for "when the weather is perceived to be cold, you should put on warm warm clothing when you go outside" results in the output of putting on a coat before going outside.

``

Are such interpretations more or less compatable with your thinking -- such that you feel that apparent feedforward phenomena are already covered by the PCT model?

``

With Regards,

Richard Pfau


Dr Warren Mansell
Reader in Psychology
Cognitive Behavioural Therapist & Chartered Clinical Psychologist
School of Psychological Sciences

Coupland I
University of Manchester
Oxford Road
Manchester M13 9PL
Email: warren.mansell@manchester.ac.uk

Tel: +44 (0) 161 275 8589

Website: http://www.psych-sci.manchester.ac.uk/staff/131406

See teamstrial.net for further information on our trial of CBT for Bipolar Disorders in NW England

The highly acclaimed therapy manual on A Transdiagnostic Approach to CBT using Method of Levels is available now.

Check www.pctweb.org for further information on Perceptual Control Theory

[From Rick Marken (2014.01.29.1120]

Richard Pfau (2014.01.28 23:36 EST)

RM: Yes, how about writing an Ashby feed-forward model of the controlling a
person does in a tracking task. Then let's see how the model works in
various versions of the task ..... Whaddaya think?

RP: Some feedback: Your suggestion for using a tracking task to test models
of feed-forward and PCT feedback is highly biased in that the PCT model
would surely perform better in such a tracking task.

RM: Not true. The experimental task should be designed to discriminate
between the feedforward and feedback only models. If it's biased to
favor one model over the other then it's a poorly designed experiment.

RP: However, if as you indicated (2012.08.21.0915) and Bill Powers seemed to
concur (2012.08.21.1217 MDT) that you "understood 'feedforward' to be
basically equivalent to what we call a reference signal"

RM: There are probably different non-feedforward explanations for
different behaviors that seem to involve feedforward. For example,
catching fly balls seems to involve feedforward; the fielder seems to
predict (anticipate) where the ball will land and runs there; in fact,
this behavior can be accounted for (nearly perfectly) with pure
feedback control model with no anticipation. I suspect that other
cases where feedforward seems to be involved can be handled by models
that involved control of a higher level perception. For example, in
one of Bill's demonstrations he shows that if you have a person track
your finger with theirs as you move yours in a circular motion, the
person will continue the circular motion for a brief time after you
stop your finger. It look like the person has been controlling finger
alignment by predicting (by extrapolation) the position of the target
finger; but my guess (and Bill's reason for developing the demo) is
that the person is controlling a higher level perception (of circular
motion) by feedback control; no feedforward is involved.

People certainly do make predictions; but these predictions are quite
high level perceptions -- like the prediction that there will be heavy
traffic to be avoided at a certain time -- and they are not always
right; but they can make it possible to compensate for disturbances
preemptively by changing the way we set lower level reference (maybe
this is what Bill was talking about when he said feedforward involved
references).

I think the best way to deal with "feedforward" may be on a case by
case basis; take specific examples of behavior and determine, by
experimental test, whether it can't be handled by the hierarchical
perceptual control model sans feedforward. PCT is, for me, my going in
offer (so to speak). I'll be happy to accept the addition of
feedforward to the model if someone is able to demonstrate to me that
the model can't account for some behavior without it. So far, no one
has demonstrated this to me.

Best

Rick

so that [here's my

···

interpretation now of what you seem to mean] (a) when someone shouts "Duck!"
then reference signals (at the relationship and program levels?) are such
that "such shouting (i.e., 'Duck!') indicates a relationship with danger"
and should be quickly acted on, whereas (b)reference signals (again at the
relationship and program levels?)for "when the weather is perceived to be
cold, you should put on warm warm clothing when you go outside" results in
the output of putting on a coat before going outside.

Are such interpretations more or less compatable with your thinking -- such
that you feel that apparent feedforward phenomena are already covered by the
PCT model?

With Regards,
Richard Pfau

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

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

[From Rick Marken (2014.01.29.1130)]

···

On Wed, Jan 29, 2014 at 6:29 AM, Warren Mansell wmansell@gmail.com wrote:

Oh, we are back here again! What a live topic! Anyone think this makes sense as an answer? I originally wrote it as a proposal for a journal article that was rejected and I need to write the full article at some point!

RM: Too bad; it’s a great proposal. Keep on trying!

Best

Rick

Predictive (or feedforward) theories of motor control have replaced simple feedback theories. Yet

the limitations of feedforward theories have been recognized and „hybrid‟ models have attempted

to reintegrate feedback, but within a secondary role. We critique hybrid models and propose that

the field would progress by „going full circle‟ to make feedback control primary. Sensory feedback

is critical because it corrects inaccurate predictions. One contemporary feedback theory

(Perceptual Control Theory; PCT) can manage delays in neural signaling, circumvent limits in

sensory feedback, and learn to optimize control. We identify four original components of PCT as

alternative ways of implementing feedforward processes to enhance feedback control over four

different, parallel timescales. We discuss the implications for a unified understanding of control.

‘Going Full Circle’: Is Control More Important Than Prediction? Warren Mansell 7/2/12

My article proposes that the „hybrid‟ models of control overemphasize prediction at the expense of

control and should be superseded by returning „full circle‟ to a feedback theory of control that

subsumes feedforward processes within its architecture.

I plan to start the article with accessible examples. At the turn of the 20th century, John Dewey

proposed that “the motor response determines the stimulus just as truly as the sensory stimulus

determines movement” - while our environment can trigger changes in our behavior, it is also the

case that our behavior is affecting the environment and we sense these effects as ‘feedback’. Take

the example of chasing a moving target during hunting – a vital skill for any predator’s survival.

Processing current sensory feedback is critical to control. The prey may change direction at any

moment, as may the angle and terrain of the ground beneath the predator’s feet. Moreover, as the

predator shifts its speed and angle to compensate, the visual image of the prey it perceives on its

retina will change instantly. The brain of the predator must process current sensory and motor

information in such a way that it regularly, and efficiently, reaches its target – or ultimately it dies.

Early 20th century engineers, recognizing the importance of feedback control, developed

technologies we take for granted today, such as thermostats, amplifiers, flight control systems, and

industrial and medical devices - some of which have an accuracy of up to four parts per million.

Within psychology, feedback models peaked with the development of ‘cybernetics’ (Ashby, 1952;

Wiener, 1948). Within a few decades, however, the pendulum had swung towards the

development of feedforward theories of control (Oosting & Dickerson, 1987). Feedforward theories

are varied, yet each computes the required actions for control based on either ongoing motor

signals or computational models that predict in advance states of the body and the environment.

Despite many decades of this research, the need for considering feedback did not go away. For

example, Optimal Control Theory was converted to a ‘hybrid’ - Optimal Feedback Control Theory –

to improve its match with observed data (Todorov & Jordan, 2002). Indeed, there are examples

across wide domains where models utilizing feedforward control rely critically on feedback

processing for accurate performance (Perkell, in press; Saunders & Vijayakumar, 2011).

The limitations of recent hybrid models are increasingly well documented, including their inability to

learn their control parameters, to engage in hierarchical control, and to control cognition

(Diedrichsen et al., 2010). Consequently, there have been recent calls for a more unified,

parsimonious theory of motor control that is directly testable (Arjemian & Hogan, 2010).

In this article, we heed this call and review evidence for a theory of feedback control that has the

capacity to restore confidence in the field: Perceptual Control Theory (PCT; Powers, Clark &

McFarland, 1960a,1960b; Powers, 1973, 2008). We make explicit four components of PCT

subsumed within its architecture that ‘feedforward’ their effects in parallel over four different time

scales (leaky integration of efferent signals, hierarchies, reorganization learning, and selfgenerated

feedback).

The article will initially critique five reasons commonly offered for the apparent advantages of

feedforward control within current „hybrid‟ models:

A. Signal delays make feedback control ineffective. This view seems inconsistent with evidence of

the performance advantages of sensory feedback during early stages of control, and for fast

movements (Boer et al., 2011; Tunik et al., 2009). Moreover, when disturbances are not

predictable, a model based on feedback processing can merely reduce its performance to the

same as that of a feedforward system. A feedback model with nerve signal delays performs

effectively over a range of simulated frequencies (Powers, 2008).

B. Sensory feedback is often unavailable. Under conditions of limited sensory feedback (e.g.

blocked vision), the brain seems capable of sensory substitution, whereby alternative streams

of sensory feedback, such as proprioception, are used (Merabet & Pascual-Leone, 2010).

C. Feedforward motor signals improve control. There are examples of feedforward motor signals,

such as the vestibulo-ocular reflex. Yet, these require stabilization through feedback processing

(Montfoort et al., 2008). The predicted sensory consequences of one’s own actions do,

however, appear to be used across a range of tasks; thus, a feedback theory needs to account

for this.

D. Control parameters for feedback systems need to be learned. Learning enhances control, yet

evidence shows that sensory feedback is typically required for this learning (Perkell, in press).

E. Internal models can predict the correct motor signal. The kinematic properties of the body and

environment are associated with specific brain regions (Grafton et al., 2008). Yet, internal

models can be complex and inaccurate (e.g. Cloete & Wallis, 2009). Thus, a parsimonious

account of internal model generation constantly updated by sensory feedback is desirable.

At this point within the article, accessible pop-out boxes will be used to explain PCT and the

evidence for the accuracy of models of behavioral tasks (Marken, 2009) and its explanation for

observed behavior (e.g. Pellis & Bell, 2011). In summary, the reference values for a feedback unit

in PCT are set by the efferent signals of a hierarchically superordinate feedback unit that operates

over a longer timescale of perceptual input. The whole system „controls perception‟, within motor

and environmental constraints. The lowest system of this hierarchy is the tendon reflex. An afferent

signal representing muscle tension is compared to an efferent reference signal entering the spinal

motor neuron, with the difference – error – being amplified by the muscle and converted to an

output force to maintain a regulated tension (Powers, 1973).

Importantly, there are four original features of PCT that could be conceptualized as ‘feed-forward’

and subserve its feedback architecture (Powers et al., 1960; Powers, 1973).

A pop-out box will

provide equations of these components and their location in a diagram. They are as follows:

(1) Leaky integration: efferent signals are integrated over periods of several milliseconds.

Therefore, the strength of a response to a disturbance in a constant direction will increase over

very brief periods. This counters signal delays (A) described above.

(2) Hierarchies: higher-level systems perceive changes that unfold over fractions of a second or

greater, and their efferent signals set the reference values for lower-level systems; these are

equivalent to the expected sensory consequences of action described above ©. For example, a

higher-level system for the desired angular position of a joint sets the desired velocity of the joint

for the system below. A mid-level system controls this variable via the efferent signals that it, in

turn, sends down to regulate acceleration via muscular forces at a lower level. Hierarchical PCT

models accurately match observed data (Marken, 1986; Powers, 2008). Future research could test

if a PCT model can utilize this information for a range of published tasks.

(3) Reorganization learning: during repeated or ongoing situations, the control parameters are

optimized through a trial-and-error learning process known as „reorganization‟, such that their

values are fed forward for improved performance. This addresses issue (D) described above.

Alternative streams of afferent input can develop through reorganization, addressing point (B).

Powers (2008) constructed and tested a computer model of 14-joint arm movement that learned its

optimal control parameters through this algorithm. Future research could test further models.

(4) Self-generated feedback: In PCT, when sensory feedback is unavailable and sensory

substitution has limited effectiveness, and/or when action is prevented, the control system can

enter ‘imagination mode’ (Powers, 1973). In this state, the reference values can be short-circuited

internally to the organism, so that approximate states of the self and world can be modeled ‘as if’

they are occurring in the environment. The motor plan that is modeled as most successful can then

be fed forward when the opportunity to control the environment is next available, subject to online

alterations through feedback control. This addresses point (E) above. Convergent evidence is

consistent with this process. Hierarchically organized closed loops of biafferent neural signals are

integral to the brain (Strick et al., 2011). Sensory deprivation in humans does indeed result in the

formation of internally generated perceptions such as spontaneous visual imagery (Mason &

Brady, 2009). Sensory deprivation in simulated robots leads to the emergence of spontaneous

internal generation of perception via a closed loop process (Gigliotta et al., 2010), and a study of

robotic systems utilizing sensory feedback models based on PCT outperformed competing models

by up to 95% (Rabinovich & Jennings, 2010).

A summary of several further advantages of adopting a PCT model of control will follow in the

article. First, a sense of control is recognised as an intrinsic need (Leotti et al., 2010), and

therefore it is appropriate that PCT places present-moment control, rather than prediction per se,

at its core. Second, cognitive control can be embodied within a PCT model (Mansell, 2011). Third,

the parsimony of PCT has made it highly adaptable to a variety of disciplines, e.g. mental health

(Carey, 2011) and organisational psychology (Vancouver & Scherbaum, 2008). Finally, the

limitations of PCT and directions for future research will be described.

Recent References

Ajemian, R., & Hogan, N. (2010). Experimenting with theoretical motor neuroscience. Journal of Motor

Behavior, 42, 333-342.

Carey, T. A. (2011). Exposure and reorganization: The what and how of effective psychotherapy. Clinical

Psychology Review, 31, 236-248.

Cloete, S. R., & Wallis, G. (2009). Limitations of feedforward control in multiple-phase steering movements.

Experimental Brain Research, 195, 481-487.

Diedrichsen, J., Shadmehr, R., & Ivry, R. B. (2010). The coordination of movement: optimal feedback control

and beyond. Trends in Cognitive Sciences, 14, 31-39.

Gigliotta, O., Pezzulo, G., & Nolfi, S. (2010). Emergences of an internal model in evolving robots subjected to

sensory deprivation. Lecture Notes in Computer Science, 6226, 575-586.

Grafton, S. T., Schmidt, P., Van Horn, J., & Diedrichsen, J. (2008). Neural substrates of visuomotor learning

based on improved feedback control and prediction. Neuroimage, 39, 1383-1395.

Leotti, L. A., Iyengar, S. S., & Ochsner, K. N. (2010). Born to choose: the origins and value of the need for

control. Trends in Cognitive Sciences, 14, 457-463.

Mansell, W. (2011). Control of perception should be operationalised as a fundamental property of the

nervous system. Topics in Cognitive Science, 3, 257-261.

Merabet, L. B., & Pascual-Leone, A. (2010). Neural reorganization following sensory loss: The opportunity of

change. Nature Reviews Neuroscience, 11, 44-52.

Marken, R. S. (2009) You say you had a revolution: Methodological foundations of closed-loop psychology.

Review of General Psychology, 13, 137-145.

Mason, O. J., & Brady, F. (2009). The psychotomimetic effects of short-term sensory deprivation. Journal of

Nervous and Mental Disease, 197, 783-785.

Montfoort, I., Van Der Geest, J. N., Slijper, H. P., De Zeeuw, C. I., & Frens, M. A. (2008). Adaptation of the

cervico- and vestibulo-ocular reflex in whiplash injury patients. Journal of Neurotrauma, 25, 687-693.

Tunik, E., Houk, J. C., & Grafton, S. T. (2009). Basal ganglia contribution to the initiation of corrective

submovements. Neuroimage, 47, 1757-1766.

Pellis, S., & Bell, H. (2011). Closing the circle between perceptions and behavior: A cybernetic view of

behavior and its consequences for studying motivation and development. Developmental Cognitive

Neuroscience, 1, 404-413.

Perkell, J. S. (in press). Movement goals and feedback and feedforward control mechanisms in speech

production. Journal of Neurolinguistics.

Saunders, I., & Vijayakumar, S. (2011). The role of feed-forward and feedback processes for closed-loop

prosthesis control. Journal of Neuroengineering and Rehabilitation, 8, 60.

Powers, W. T. (2008). Living Control Systems III: The Fact of Control. Benchmark Publications.

Rabinovich, Z., & Jennings, N. R. (2010). A hybrid controller based on the egocentric perceptual principle.

Robotics and Autonomous Systems, 58, 1039-1048.

Strick, P. L., Dum, R. P., Fiez, J. A. (2011). Cerebellum and Nonmotor Function. The Annual Review of

Neuroscience, 32, 413-434.

Vancouver, J. B. & Scherbaum, C. A. (2008). Do we self-regulate actions or perceptions? A test of two

computational models. Computational and Mathematical Organizational Theory, 14, 1-22.

Earlier References

Ashby, W. R. (1952). A Design for a Brain. Chapman & Hall.

Oosting, K. W. & Dickerson, S. L. (1987). Feed forward control for stabilisation. ASME

Powers, W. T. (1973). Behavior: The Control of Perception. Chicago, IL: Aldine.

Powers, W. T., Clark, R. K., & McFarland, R. L. (1960a). A general feedback theory of human behavior. Part

I. Perceptual and Motor Skills, 11, 71-88.

Powers, W. T., Clark, R. K., & McFarland, R. L. (1960). A general feedback theory of human behavior. Part

II. Perceptual and Motor Skills, 11, 309-323.

Todorov, E., & Jordan, M. I. (2002). Optimal feedback control as a theory of motor coordination. Nature

Neuroscience, 5, 1226-1235.

Wiener, N. (1948). Cybernetics: Or Control and Communication in the Animal and the Machine. MIT Press.


Richard S. Marken PhD
www.mindreadings.com
The only thing that will redeem mankind is cooperation.

                                               -- Bertrand Russell

On Wed, Jan 29, 2014 at 4:35 AM, Richard H. Pfau richardpfau4153@aol.com wrote:

[From Richard Pfau (2014.01.28 23:36 EST)]

`Ref: [From Rick Marken (2014.01.28.1620)]

RM: Yes, how about writing an Ashby feed-forward model of the controlling a person does in a tracking task. Then let’s see how the model works in various versions of the task … Whaddaya think?

RP: Some feedback: Your suggestion for using a tracking task to test models of feed-forward and PCT feedback is highly biased in that the PCT model would surely perform better in such a tracking task.`

And a thought: In some cases, the phenomena called feedforward does seem to be taking place. (a) Bruce's example of the wrecking ball seems like one. (b) Another seems to be when you put on a coat when it's cold outside (i.e., before you are physically cold) to prevent yourself from becoming chilled when you go outside.

``

However, if as you indicated (2012.08.21.0915) and Bill Powers seemed to concur (2012.08.21.1217 MDT) that you "understood 'feedforward' to be basically equivalent to what we call a reference signal" so that [here's my interpretation now of what you seem to mean] (a) when someone shouts "Duck!" then reference signals (at the relationship and program levels?) are such that "such shouting (i.e., 'Duck!') indicates a relationship with danger" and should be quickly acted on, whereas (b)reference signals (again at the relationship and program levels?)for "when the weather is perceived to be cold, you should put on warm warm clothing when you go outside" results in the output of putting on a coat before going outside.

``

Are such interpretations more or less compatable with your thinking -- such that you feel that apparent feedforward phenomena are already covered by the PCT model?

``

With Regards,

Richard Pfau


Dr Warren Mansell
Reader in Psychology
Cognitive Behavioural Therapist & Chartered Clinical Psychologist
School of Psychological Sciences

Coupland I
University of Manchester
Oxford Road
Manchester M13 9PL
Email: warren.mansell@manchester.ac.uk

Tel: +44 (0) 161 275 8589

Website: http://www.psych-sci.manchester.ac.uk/staff/131406

See teamstrial.net for further information on our trial of CBT for Bipolar Disorders in NW England

The highly acclaimed therapy manual on A Transdiagnostic Approach to CBT using Method of Levels is available now.

Check www.pctweb.org for further information on Perceptual Control Theory

[From Bruce Abbott (2014.01.29.1450 EST)]

Rick Marken (2014.01.28.1825)] --

Bruce Abbott (2014.01.28.2105 EST)

BA: We already know that humans doing the standard tracking task
usually do an excellent job of controlling, especially after they've
had some practice at it. It's a situation where feed forward control
could not offer much of an advantage.

RM: I was thinking of a pursuit tracking task, where the disturbance (target
movement) is quite visible. That's a situation where it is known that
tracking a predictable disturbance (like a sine wave) can be quite a bit
better than tracking an unpredictable one (other things being as equal as
you can make them). It looks like feedforward might be involved there,
right?

BA: It might be. But I've been thinking about Ashby's feed forward model in
the context of human/animal systems and doubt that this exact model is
realized there. Ashby's feed forward system relies on (a) sensing the
disturbance ahead of its potential effect on the controlled variable, (b)
generating an inverse effect that mirrors that of the disturbance, and (c)
applying it to the controlled variable exactly in phase with the
disturbance, so that the disturbance and opponent variable exactly cancel
out. Such a system is theoretically realizable but I suspect difficult to
carry out with the necessary precision in the human/animal behavioral case.
Moreover, there is the problem of how the opponent action is to be
generated. An electronic system would contain circuitry that inverts the
disturbance signal to generate the opposing variable, and uses this to drive
the actuators. This is just a simple stimulus-response mechanism that
applies its "response," appropriately timed, to the variable for which the
effect of the disturbance is to be neutralized. On the other hand, muscles
are driven by negative-feedback control system outputs, which implies that
the sensed disturbance somehow would have to serve either as a disturbance
or produce changes in reference levels for these systems.

In the pursuit-tracking case with a sinusoidal disturbance, the participant
learns to generate an approximately sinusoidal output that, in its effect on
the controlled variable, has the same frequency and amplitude as the
disturbance but is 180 degrees out of phase with it. The person must learn
to do this and even after much practice, most people are not very good at
it. How people are able to generate such a pattern is not well understood --
it might done by comparing perceived movements to those specified by a
remembered temporally-extended reference pattern. The participant would
need to adjust his or her perception of the amplitude and phase of the
cursor movements while maintaining the sinusoidal pattern. To use the
perceived pattern of target movements in a feed forward system, the
participant would have to use the current position of the target within the
repeating pattern to generate an output that is phase-shifted forward just
enough to compensate for the changes taking place in target position a
moment later. But notice how the participant is still using negative
feedback control to adjust the pattern, amplitude, and phase of the output
compensatory action in order to create the fed-forward pattern. Although the
phase-shifted output technically qualifies as a fed forward compensatory
action, I think that the system is more adequately described as a feedback
system in which phase is a controlled variable.

Bruce