What's an internal model?

[From Rupert Young (2014.09.12 15.00)]

I have attached this interesting paper:

"The role of feed-forward and feedback processes for closed-loop
prosthesis control"

which has the conclusion,

"... we concluded that subjects were able to learn an internal model in
the absence of within-trial feedback. We posit that a feedforward
process was playing a crucial role in the observed behaviour."

I'd be interested in whether, and how, others think the results could be
interpreted in a PCT context.

saunders-JNER2011.pdf (2.38 MB)

···

--

Regards,
Rupert

[From Richard Kennaway (2014.09.12 15:16)]

Rupert Young asks "What's an internal model?"

from Wikipedia http://en.wikipedia.org/wiki/Internal_model_(motor_control)
"An internal model is a postulated neural process that simulates the response of the motor system in order to estimate the outcome of a motor command."

I looked at a few other references, and the concept goes back to a 1990 paper by Masao Ito, "A new physiological concept on cerebellum" (probably paywalled, but the reference is at http://www.ncbi.nlm.nih.gov/pubmed/2263818).

I couldn't quickly find any papers discussing evidence for or against the hypothesis, only papers starting from the hypothesis and building on it.

Masao Ito is one of the big names in cerebellar studies, and has a book on his approach to the subject: http://www.amazon.co.uk/The-Cerebellum-Brain-Implicit-Science-ebook/dp/B005DKQQG4.

Oh, here's another quote that, um, well. "Control can be achieved only if the control system encapsulates, either implicitly or explicitly, some representation of the process to be controlled." From some lecture notes on Robust Control at http://lorien.ncl.ac.uk/ming/robust/imc.pdf. PID loops control a lot of things -- where are the models that this claims they must contain?

···

--
Richard Kennaway, R.Kennaway@uea.ac.uk, http://www.cmp.uea.ac.uk/~jrk/
School of Computing Sciences,
University of East Anglia, Norwich NR4 7TJ, U.K.

[From Frans X. Plooij (2014.09.12 16:52)]

And what about this:

Raspopovic, Stanisa, Capogrosso, Marco, Petrini, Francesco Maria, Bonizzato, Marco, Rigosa, Jacopo, Di Pino, Giovanni, . . . Micera, Silvestro. (2014). Restoring Natural Sensory Feedback in Real-Time Bidirectional Hand Prostheses. Science Translational Medicine, 6(222), 222ra219. doi: 10.1126/scitranslmed.3006820

Abstract:

Hand loss is a highly disabling event that markedly affects the quality of life. To achieve a close to natural replacement for the lost hand, the user should be provided with the rich sensations that we naturally perceive when grasping or manipulating an object. Ideal bidirectional hand prostheses should involve both a reliable decoding of the user’s intentions and the delivery of nearly “natural” sensory feedback through remnant afferent pathways, simultaneously and in real time. However, current hand prostheses fail to achieve these requirements, particularly because they lack any sensory feedback. We show that by stimulating the median and ulnar nerve fascicles using transversal multichannel intrafascicular electrodes, according to the information provided by the artificial sensors from a hand prosthesis, physiologically appropriate (near-natural) sensory information can be provided to an amputee during the real-time decoding of different grasping tasks to control a dexterous hand prosthesis. This feedback enabled the participant to effectively modulate the grasping force of the prosthesis with no visual or auditory feedback. Three different force levels were distinguished and consistently used by the subject. The results also demonstrate that a high complexity of perception can be obtained, allowing the subject to identify the stiffness and shape of three different objects by exploiting different characteristics of the elicited sensations. This approach could improve the efficacy and “life-like” quality of hand prostheses, resulting in a keystone strategy for the near-natural replacement of missing hands.

···

Op 12 sep. 2014, om 15:41 heeft Rupert Young rupert@moonsit.co.uk het volgende geschreven:

[From Rupert Young (2014.09.12 15.00)]

I have attached this interesting paper:

“The role of feed-forward and feedback processes for closed-loop
prosthesis control”

which has the conclusion,

“… we concluded that subjects were able to learn an internal model in
the absence of within-trial feedback. We posit that a feedforward
process was playing a crucial role in the observed behaviour.”

I’d be interested in whether, and how, others think the results could be
interpreted in a PCT context.

Regards,
Rupert

<saunders-JNER2011.pdf>

[Martin Taylor 2014.09.12.11.30]

[From Richard Kennaway (2014.09.12 15:16)]

Rupert Young asks "What's an internal model?"

...

Oh, here's another quote that, um, well. "Control can be achieved only if the control system encapsulates, either implicitly or explicitly, some representation of the process to be controlled." From some lecture notes on Robust Control at http://lorien.ncl.ac.uk/ming/robust/imc.pdf. PID loops control a lot of things -- where are the models that this claims they must contain?

I won't talk to single loops, but a well-reorganized control hierarchy does contain models. They are encapsulated in the inter-level connection weights and functions.

The the authors of the paper Rupert sent say that the models are needed in order to reduce "feedforward uncertainty". PCT handles that issue very neatly. When the set of higher-level outputs produces a reference value in a lower-level control unit, that control unit returns a controlled perception. In an intact human that doesn't have some neurological pathology, low-level systems usually control quite well, which means that the higher-level systems can reasonably reliably get the results they expect from their outputs, even though outputs are never controlled. In contrast to an explicitly modelled feedforward system that behaves always as it should (no disturbances), the PCT hierarchy gets the same effect in the presence of disturbances.

As Richard says, a single PID loop controls a lot of things with no model other than gain and bandwidth limitations to avoid lag-induced oscillation. But if that is all that is ever needed, why reorganize at all? PCT does implicitly describe the construction of world models, and Bill's Artificial Cerebellum did so explicitly. The PCT model of how the world works (not what is in the world) is contained in the very large set of weights and logical relationships that describe the inter-level connections on both input and output side, and its model of what is in the world (perhaps including some of how the world works) is in the current set of ever-changing perceptual values.

Martin

[From Rick Marken (2014.09.12.1300)]

···

Frans X. Plooij (2014.09.12 16:52)

And what about this:

Raspopovic, Stanisa, Capogrosso, Marco, Petrini, Francesco Maria, Bonizzato, Marco, Rigosa, Jacopo, Di Pino, Giovanni, . . . Micera, Silvestro. (2014). Restoring Natural Sensory Feedback in Real-Time Bidirectional Hand Prostheses. Science Translational Medicine, 6(222), 222ra219. doi: 10.1126/scitranslmed.3006820

Abstract:

… We show that by stimulating the median and ulnar nerve fascicles using transversal multichannel intrafascicular electrodes, according to the information provided by the artificial sensors from a hand prosthesis, physiologically appropriate (near-natural) sensory information can be provided to an amputee during the real-time decoding of different grasping tasks to control a dexterous hand prosthesis. This feedback enabled the participant to effectively modulate the grasping force of the prosthesis with no visual or auditory feedback…

RM: Or this:

http://www.cbsnews.com/news/amputee-gets-first-prosthetic-hand-lifehand-that-allows-sense-of-touch/

RM: This research was led by a Dr. Paolo Rossini, so it’s a different group than the one you found. But same result; better control of grasping with prosthetic sensory perception, allowing control of perception.

RM: By the way, Frans, I was on jury duty yesterday, which meant sitting around in a room all day with nothing to do but read and hope that I didn’t get called to sit on a jury. So I brought your 1984 monograph “Behavioral Development of Free Living Chimpanzee Babies and Infants” to read so that I could correctly describe (in future papers) what you found – and how you found it – and, though I didn’t finish it yesterday, I was really impressed by your work. This is what I would really love to see more of; actual observational research based on PCT. And your description of how to analyze the data to look for controlled variables and levels of controlled variables (on pp. 14 and 15) is stupendous. This is a very technical book but must reading for anyone interested in doing PCT based research (like me;-).

Best regards

Rick


Richard S. Marken, Ph.D.
Author of Doing Research on Purpose.
Now available from Amazon or Barnes & Noble

[From Adam Matic 2014.09.13]

Rupert Young (2014.09.12 15.00)

I have attached this interesting paper:

"The role of feed-forward and feedback processes for closed-loop prosthesis control"

which has the conclusion,

"... we concluded that subjects were able to learn an internal model in the absence of within-trial feedback. We posit that a feedforward process was playing a crucial role in the observed behaviour."

I'd be interested in whether, and how, others think the results could be interpreted in a PCT context.

from what I've read in the literature on motor control, it seems some researchers, like mentioned Ito, M. Kawato, D. Wolpert and many others, are actually making models, simulations and robots based on the modern control theory (the engineering field) and they actually make a mathematical 'internal model', a simulation of the environment, inside of a feedback loop. Their assumption is either (a) that somewhere in the brain there are models exactly like that or (b), that the brain does it in a different way, but still has to do it.
Simulations run fine, robots worse. I haven't actually programmed one, and I wouldn't know how, they seem very complex. Bulk of published criticism comes from the Equilibrium Point hypothesis (EP) crowd (Feldman, Latash, ...) who say that there is simply no way the brain could implement something as complex as that, and that the EP is more biologically plausible. Which is true in a way. They are still fighting over this, they keep revising the EP because it doesn't work very well.

···

----
On the other hand, guys who wrote this paper, use 'feedback' to mean simply sensory information sent 'back to the subject' from the arm, and feedforward' as commands going from the subject to the arm. Their use of the expression 'internal model' is loose, it means 'whatever is sending those signals toward the robot arm'.
And one of their conclusion is: "we must develop prostheses that empower users to correct for the inevitable uncertainty in their feed-forward control". Which sounds great, because visual only loops make it hard for people to control prosthetic arms. They use haptic feedback for help in grasping, and it seems to me they might benefit from joint-angle feedback as well, and a bunch of other sensors.
Adam

[From Rupert Young (2014.09.13 13.00)]

What my question was leading to was, is the PCT structure of reference signals 'internal models', as the paper authors would recognise them?

As Adam notes they are very loose with their definition of 'internal model'. They are quite vague about what it is and rather than define its structure and function they infer that it exists due to previous research. In this research their evidential support seems to be entirely based upon the observation that the subjects are able to distinguish between the weight of the two objects.

"Nevertheless, subjects still differentiated the two objects, which requires precise signal timing in order to set
appropriate grasp forces. Since the objects were lifted multiple times, we concluded that subjects were able to
learn an internal model in the absence of within-trial feedback. We posit that a feedforward process was playing
a crucial role in the observed behaviour."

They also go on to say,

"Furthermore, subjects were aware of a successful lift via feedback from their arm muscles as
well as on-screen feedback at the end of each trial, allowing them to refine their judgements. Our work
assumes that, by these processes, subjects can establish a feedforward prediction. This is defined as the ability to
anticipate the forces they are exerting in the absence of externally-arising cues to that fact (see introduction)."

This sounds quite consistent with PCT learning of a higher-level system setting a lower reference signal (which Martin is calling a "model") which would initiate action prior to what would have happened with the single level, i.e. anticipation (rather than prediction).

So the question is, are we and they talking about the same thing? Is there a fundamental difference? Perhaps, the definition posted by Richard K highlights that difference.

Incidentally, they were claiming that a feedforward process was occuring due to absence of feedback (visual, tactile, auditory) yet, as they state above, there was feedback from arm muscles, so surely that could be a candidate for object weight discrimination.

Related to this point they seem to be denying the role of feedback in some cases when they say,

"It is important to note that proprioceptive and tactile cues of the control signal are considered to be internal cues
– they provide no feedback of how the robotic hand is interacting with the environment."

What do they mean by that?
Regards,
Rupert

···

On 13/09/2014 01:58, Adam Matic wrote:

[From Adam Matic 2014.09.13]

Rupert Young (2014.09.12 15.00)

I have attached this interesting paper:

"The role of feed-forward and feedback processes for closed-loop prosthesis control"

which has the conclusion,

"... we concluded that subjects were able to learn an internal model in the absence of within-trial feedback. We posit that a feedforward process was playing a crucial role in the observed behaviour."

I'd be interested in whether, and how, others think the results could be interpreted in a PCT context.

From what I've read in the literature on motor control, it seems some researchers, like mentioned Ito, M. Kawato, D. Wolpert and many others, are actually making models, simulations and robots based on the modern control theory (the engineering field) and they actually make a mathematical 'internal model', a simulation of the environment, inside of a feedback loop. Their assumption is either (a) that somewhere in the brain there are models exactly like that or (b), that the brain does it in a different way, but still has to do it.
Simulations run fine, robots worse. I haven't actually programmed one, and I wouldn't know how, they seem very complex. Bulk of published criticism comes from the Equilibrium Point hypothesis (EP) crowd (Feldman, Latash, ...) who say that there is simply no way the brain could implement something as complex as that, and that the EP is more biologically plausible. Which is true in a way. They are still fighting over this, they keep revising the EP because it doesn't work very well.
----
On the other hand, guys who wrote this paper, use 'feedback' to mean simply sensory information sent 'back to the subject' from the arm, and feedforward' as commands going from the subject to the arm. Their use of the expression 'internal model' is loose, it means 'whatever is sending those signals toward the robot arm'.Â
And one of their conclusion is: "we must develop prostheses that empower users to correct for the inevitable uncertainty in their feed-forward control". Which sounds great, because visual only loops make it hard for people to control prosthetic arms. They use haptic feedback for help in grasping, and it seems to me they might benefit from joint-angle feedback as well, and a bunch of other sensors.
Adam

[Rick Marken (201409.13.1200)]

···

Rupert Young (2014.09.13 13.00)–

  RY: What my question was leading to was, is the PCT structure of

reference signals ‘internal models’, as the paper authors would
recognise them?

RM: I think “internal models” can refer to two different things. One is what Adam referred to: models of the physical environment that allow “feedforward” predictions of the effect of system output on the controlled variable. This kind of internal model can improve control somewhat in a completely predictable (and, thus, artificial) environment but not so much in a real one where disturbances are upredictable. This is why, as Adam puts it succinctly “Simulations [with internal models:RM] run fine, robots worse”. There is really nothing in PCT that corresponds to this kind of “internal model”.

RM: The other kind of “internal model” corresponds to a higher level controlled perception in PCT. Although I haven’t read the Saunders “Feedforward and prosthesis control” paper carefully, my impression is that the improved gripping with vision results from the subjects’ ability to control a higher level visual variable that could not be controlled when the prosthesis was out of sight.

RM: The desire to show that control results from output calculation (feedforward) is baffling to me. But it’s there and it makes it very difficult to get people to pay attention to PCT, particularly because (as Adam also notes) "the published criticism [of the calculated output approach to control:RM] comes from the Equilibrium Point hypothesis (EP) crowd (Feldman, Latash, …) " which, as Adam politely notes “doesn’t work very well” (since it is not a control model:RM).

RM: I think what is common to both the calculated output and EP approaches to control is fear of purposiveness; both aim to “account for” control (apparent purposiveness) in terms of lineal causal laws. Unfortunately, the purposiveness of control is not apparent; it is a fact. Ergo my continued harping on the fact of control. I really think that is the big deal about PCT: PCT shows us that the behavior of living system is control (purposeful), in fact, not just in theory. PCT shows how to demonstrate this fact and how to explain it.

Best regards

Rick


Richard S. Marken, Ph.D.
Author of Doing Research on Purpose.
Now available from Amazon or Barnes & Noble

[From Frans X. Plooij (2014.09.14 16:13)]

Thanks for the article Rick and for the compliments.

Best, Frans

···

Frans X. Plooij (2014.09.12 16:52)

And what about this:

Raspopovic, Stanisa, Capogrosso, Marco, Petrini, Francesco Maria, Bonizzato, Marco, Rigosa, Jacopo, Di Pino, Giovanni, . . . Micera, Silvestro. (2014). Restoring Natural Sensory Feedback in Real-Time Bidirectional Hand Prostheses. Science Translational Medicine, 6(222), 222ra219. doi: 10.1126/scitranslmed.3006820

Abstract:

… We show that by stimulating the median and ulnar nerve fascicles using transversal multichannel intrafascicular electrodes, according to the information provided by the artificial sensors from a hand prosthesis, physiologically appropriate (near-natural) sensory information can be provided to an amputee during the real-time decoding of different grasping tasks to control a dexterous hand prosthesis. This feedback enabled the participant to effectively modulate the grasping force of the prosthesis with no visual or auditory feedback…

RM: Or this:

http://www.cbsnews.com/news/amputee-gets-first-prosthetic-hand-lifehand-that-allows-sense-of-touch/

RM: This research was led by a Dr. Paolo Rossini, so it’s a different group than the one you found. But same result; better control of grasping with prosthetic sensory perception, allowing control of perception.

RM: By the way, Frans, I was on jury duty yesterday, which meant sitting around in a room all day with nothing to do but read and hope that I didn’t get called to sit on a jury. So I brought your 1984 monograph “Behavioral Development of Free Living Chimpanzee Babies and Infants” to read so that I could correctly describe (in future papers) what you found – and how you found it – and, though I didn’t finish it yesterday, I was really impressed by your work. This is what I would really love to see more of; actual observational research based on PCT. And your description of how to analyze the data to look for controlled variables and levels of controlled variables (on pp. 14 and 15) is stupendous. This is a very technical book but must reading for anyone interested in doing PCT based research (like me;-).

Best regards

Rick


Richard S. Marken, Ph.D.
Author of Doing Research on Purpose.
Now available from Amazon or Barnes & Noble

Feedback and feedforward seem to have been reduced to synonyms for afferent and efferent, with no sense of a loop. This terminological bushwhack needs to be recognized and countered explicitly in any presentation, publication, or conversation. (Same with controlled variable vs. the several senses of ‘control variable’.)

There is a third sense of the term ‘internal model’, along the lines that Martin was talking about. The structure of the control hierarchy–the interconnections and weights of connections, the perceptual input functions, the reference input functions, and tje output functions–constitute a kind of model of the environment in which that control hierarchy controls successfully.

These folks seem to be trying to implement the ‘cognitive maps’ kind of notion promulgated in Cognitive Psychology: sensory data come in, are correlated with cognitive maps, ‘information processing’ takes place, and the computer in the skull issues commands to muscles.

···

On Sat, Sep 13, 2014 at 2:58 PM, Richard Marken rsmarken@gmail.com wrote:

[Rick Marken (201409.13.1200)]

Rupert Young (2014.09.13 13.00)–

  RY: What my question was leading to was, is the PCT structure of

reference signals ‘internal models’, as the paper authors would
recognise them?

RM: I think “internal models” can refer to two different things. One is what Adam referred to: models of the physical environment that allow “feedforward” predictions of the effect of system output on the controlled variable. This kind of internal model can improve control somewhat in a completely predictable (and, thus, artificial) environment but not so much in a real one where disturbances are upredictable. This is why, as Adam puts it succinctly “Simulations [with internal models:RM] run fine, robots worse”. There is really nothing in PCT that corresponds to this kind of “internal model”.

RM: The other kind of “internal model” corresponds to a higher level controlled perception in PCT. Although I haven’t read the Saunders “Feedforward and prosthesis control” paper carefully, my impression is that the improved gripping with vision results from the subjects’ ability to control a higher level visual variable that could not be controlled when the prosthesis was out of sight.

RM: The desire to show that control results from output calculation (feedforward) is baffling to me. But it’s there and it makes it very difficult to get people to pay attention to PCT, particularly because (as Adam also notes) "the published criticism [of the calculated output approach to control:RM] comes from the Equilibrium Point hypothesis (EP) crowd (Feldman, Latash, …) " which, as Adam politely notes “doesn’t work very well” (since it is not a control model:RM).

RM: I think what is common to both the calculated output and EP approaches to control is fear of purposiveness; both aim to “account for” control (apparent purposiveness) in terms of lineal causal laws. Unfortunately, the purposiveness of control is not apparent; it is a fact. Ergo my continued harping on the fact of control. I really think that is the big deal about PCT: PCT shows us that the behavior of living system is control (purposeful), in fact, not just in theory. PCT shows how to demonstrate this fact and how to explain it.

Best regards

Rick


Richard S. Marken, Ph.D.
Author of Doing Research on Purpose.
Now available from Amazon or Barnes & Noble

[From Rick Marken (2015.10.21.1315)]

···

On Tue, Oct 20, 2015 at 2:00 PM, Bruce Nevin bnhpct@gmail.com wrote:

BN: Feedback and feedforward seem to have been reduced to synonyms for afferent and efferent, with no sense of a loop. This terminological bushwhack needs to be recognized and countered explicitly in any presentation, publication, or conversation. (Same with controlled variable vs. the several senses of ‘control variable’.)

RM: I agree. But I think the best way to “combat” this kind of misunderstanding of perceptual control to reduce the time spent countering these ideas and increase the time spent doing research (and application) based on PCT. I say this as one who is perhaps the most guilty of having spent way too much time countering these “open loop” ideas (in presentations, publications, and conversations) and far too little time doing PCT research.

RM: The question about the future of PCT made me realize that one of the big impediments to the adoption of PCT (by researchers anyway) – one that I hadn’t thought of before – is that people who might be interested in doing such research don’t really know how to do it. People trained in experimental or physiological psychology have been shown the kind of research that is done in the field; they have lots of models for what counts as a good research. But there are few such models for doing research based on PCT.

RM: PCT research is going to be very different than the kind of research that is familiar to conventional experimental/physiological psychologists. The questions a PCT researcher asks about behavior are completely different than those asked by a conventional researcher. So when a conventional psychologist gets interested in PCT he is either put off by the fact that he doesn’t know how to study it or assumes that the way you study it is the way you have been studying behavior all along and the result is the Carver/Scheier approach to PCT.

RM: DRoP (which you so kindly reviewed) is aimed at showing people how to do PCT research. But I think it might be a little too abstract, even for people who are already involved in psychological research. I think what I will have to do is write another book that is less formal and includes lots of suggestions for the kind of research one might do to test PCT; a book that puts a little more “flesh on the bones” of PCT research. If we can get a critical number of researchers doing PCT(what that number is, I don;'t know, but I’m pretty sure it’s > 3) and, thus, providing models for other researchers to imitate, I think we will then see PCT start to have a greater influence in the life sciences.

Best

Rick

There is a third sense of the term ‘internal model’, along the lines that Martin was talking about. The structure of the control hierarchy–the interconnections and weights of connections, the perceptual input functions, the reference input functions, and tje output functions–constitute a kind of model of the environment in which that control hierarchy controls successfully.

These folks seem to be trying to implement the ‘cognitive maps’ kind of notion promulgated in Cognitive Psychology: sensory data come in, are correlated with cognitive maps, ‘information processing’ takes place, and the computer in the skull issues commands to muscles.

/BN


Richard S. Marken

www.mindreadings.com
Author of Doing Research on Purpose.
Now available from Amazon or Barnes & Noble

On Sat, Sep 13, 2014 at 2:58 PM, Richard Marken rsmarken@gmail.com wrote:

[Rick Marken (201409.13.1200)]

Rupert Young (2014.09.13 13.00)–

  RY: What my question was leading to was, is the PCT structure of

reference signals ‘internal models’, as the paper authors would
recognise them?

RM: I think “internal models” can refer to two different things. One is what Adam referred to: models of the physical environment that allow “feedforward” predictions of the effect of system output on the controlled variable. This kind of internal model can improve control somewhat in a completely predictable (and, thus, artificial) environment but not so much in a real one where disturbances are upredictable. This is why, as Adam puts it succinctly “Simulations [with internal models:RM] run fine, robots worse”. There is really nothing in PCT that corresponds to this kind of “internal model”.

RM: The other kind of “internal model” corresponds to a higher level controlled perception in PCT. Although I haven’t read the Saunders “Feedforward and prosthesis control” paper carefully, my impression is that the improved gripping with vision results from the subjects’ ability to control a higher level visual variable that could not be controlled when the prosthesis was out of sight.

RM: The desire to show that control results from output calculation (feedforward) is baffling to me. But it’s there and it makes it very difficult to get people to pay attention to PCT, particularly because (as Adam also notes) "the published criticism [of the calculated output approach to control:RM] comes from the Equilibrium Point hypothesis (EP) crowd (Feldman, Latash, …) " which, as Adam politely notes “doesn’t work very well” (since it is not a control model:RM).

RM: I think what is common to both the calculated output and EP approaches to control is fear of purposiveness; both aim to “account for” control (apparent purposiveness) in terms of lineal causal laws. Unfortunately, the purposiveness of control is not apparent; it is a fact. Ergo my continued harping on the fact of control. I really think that is the big deal about PCT: PCT shows us that the behavior of living system is control (purposeful), in fact, not just in theory. PCT shows how to demonstrate this fact and how to explain it.

Best regards

Rick

Richard S. Marken, Ph.D.
Author of Doing Research on Purpose.
Now available from Amazon or Barnes & Noble

RM: DRoP … is aimed at showing people how to do PCT research. But I think it might be a little too abstract, even for people who are already involved in psychological research. I think what I will have to do is write another book that is less formal and includes lots of suggestions for the kind of research one might do to test PCT; a book that puts a little more “flesh on the bones” of PCT research.

BN: Music to my ears! I have been hoping you would take this up. Yes!

BN: In laboratory experiments, input, output, and disturbance quantities can be precisely measured, and extraneous disturbances can be perceived and controlled to zero by the experimenter. Methodological help is also needed for naturalistic work in conditions that cannot be so precisely controlled, for practical reasons and sometimes for ethical reasons. A huge proportion of such situations involve collective control, especially where elements of the environmental feedback paths are collectively controlled. Kent’s chapter for LCS IV lays out the issues and principles brilliantly. (I’m reading part 2 now.) But methodological help is needed with research into collective control.

RM: If we can get a critical number of researchers doing PCT … and … providing models for other researchers to imitate, I think we will then see PCT start to have a greater influence in the life sciences.

BN: People’s interest perks up with socially significant variables. Baseball catching has greater intrinsic interest than pursuit tracking, but its grounding in the game of baseball only brings it just over the horizon of social significance. There is enormous need for understanding our participation in collective control, and for understanding social change, because there is a great deal of social change going on, and much more of it in months and years shortly to come. We know that a grasp of PCT brings with it a radical change in our perception of rewards and punishments and the nature of motivation. Such change is needed in our institutions and social norms. An understanding of collective control, and how pervasive it is, and how necessary, similarly shrinks the narcissistic fantasy called ‘independence’ to a perspective that is more modest and more capable of autonomous control. (The difference between independence and autonomy.)

···

On Wed, Oct 21, 2015 at 4:15 PM, Richard Marken rsmarken@gmail.com wrote:

[From Rick Marken (2015.10.21.1315)]

On Tue, Oct 20, 2015 at 2:00 PM, Bruce Nevin bnhpct@gmail.com wrote:

BN: Feedback and feedforward seem to have been reduced to synonyms for afferent and efferent, with no sense of a loop. This terminological bushwhack needs to be recognized and countered explicitly in any presentation, publication, or conversation. (Same with controlled variable vs. the several senses of ‘control variable’.)

RM: I agree. But I think the best way to “combat” this kind of misunderstanding of perceptual control to reduce the time spent countering these ideas and increase the time spent doing research (and application) based on PCT. I say this as one who is perhaps the most guilty of having spent way too much time countering these “open loop” ideas (in presentations, publications, and conversations) and far too little time doing PCT research.

RM: The question about the future of PCT made me realize that one of the big impediments to the adoption of PCT (by researchers anyway) – one that I hadn’t thought of before – is that people who might be interested in doing such research don’t really know how to do it. People trained in experimental or physiological psychology have been shown the kind of research that is done in the field; they have lots of models for what counts as a good research. But there are few such models for doing research based on PCT.

RM: PCT research is going to be very different than the kind of research that is familiar to conventional experimental/physiological psychologists. The questions a PCT researcher asks about behavior are completely different than those asked by a conventional researcher. So when a conventional psychologist gets interested in PCT he is either put off by the fact that he doesn’t know how to study it or assumes that the way you study it is the way you have been studying behavior all along and the result is the Carver/Scheier approach to PCT.

RM: DRoP (which you so kindly reviewed) is aimed at showing people how to do PCT research. But I think it might be a little too abstract, even for people who are already involved in psychological research. I think what I will have to do is write another book that is less formal and includes lots of suggestions for the kind of research one might do to test PCT; a book that puts a little more “flesh on the bones” of PCT research. If we can get a critical number of researchers doing PCT(what that number is, I don;'t know, but I’m pretty sure it’s > 3) and, thus, providing models for other researchers to imitate, I think we will then see PCT start to have a greater influence in the life sciences.

Best

Rick

There is a third sense of the term ‘internal model’, along the lines that Martin was talking about. The structure of the control hierarchy–the interconnections and weights of connections, the perceptual input functions, the reference input functions, and tje output functions–constitute a kind of model of the environment in which that control hierarchy controls successfully.

These folks seem to be trying to implement the ‘cognitive maps’ kind of notion promulgated in Cognitive Psychology: sensory data come in, are correlated with cognitive maps, ‘information processing’ takes place, and the computer in the skull issues commands to muscles.

/BN


Richard S. Marken

www.mindreadings.com
Author of Doing Research on Purpose.
Now available from Amazon or Barnes & Noble

On Sat, Sep 13, 2014 at 2:58 PM, Richard Marken rsmarken@gmail.com wrote:

[Rick Marken (201409.13.1200)]

Rupert Young (2014.09.13 13.00)–

  RY: What my question was leading to was, is the PCT structure of

reference signals ‘internal models’, as the paper authors would
recognise them?

RM: I think “internal models” can refer to two different things. One is what Adam referred to: models of the physical environment that allow “feedforward” predictions of the effect of system output on the controlled variable. This kind of internal model can improve control somewhat in a completely predictable (and, thus, artificial) environment but not so much in a real one where disturbances are upredictable. This is why, as Adam puts it succinctly “Simulations [with internal models:RM] run fine, robots worse”. There is really nothing in PCT that corresponds to this kind of “internal model”.

RM: The other kind of “internal model” corresponds to a higher level controlled perception in PCT. Although I haven’t read the Saunders “Feedforward and prosthesis control” paper carefully, my impression is that the improved gripping with vision results from the subjects’ ability to control a higher level visual variable that could not be controlled when the prosthesis was out of sight.

RM: The desire to show that control results from output calculation (feedforward) is baffling to me. But it’s there and it makes it very difficult to get people to pay attention to PCT, particularly because (as Adam also notes) "the published criticism [of the calculated output approach to control:RM] comes from the Equilibrium Point hypothesis (EP) crowd (Feldman, Latash, …) " which, as Adam politely notes “doesn’t work very well” (since it is not a control model:RM).

RM: I think what is common to both the calculated output and EP approaches to control is fear of purposiveness; both aim to “account for” control (apparent purposiveness) in terms of lineal causal laws. Unfortunately, the purposiveness of control is not apparent; it is a fact. Ergo my continued harping on the fact of control. I really think that is the big deal about PCT: PCT shows us that the behavior of living system is control (purposeful), in fact, not just in theory. PCT shows how to demonstrate this fact and how to explain it.

Best regards

Rick

Richard S. Marken, Ph.D.
Author of Doing Research on Purpose.
Now available from Amazon or Barnes & Noble

[From Rick Marken (2015.10.23.0840)]

···

On Wed, Oct 21, 2015 at 4:20 PM, Bruce Nevin bnhpct@gmail.com wrote:

RM: DRoP … is aimed at showing people how to do PCT research. But I think it might be a little too abstract, even for people who are already involved in psychological research. I think what I will have to do is write another book that is less formal and includes lots of suggestions for the kind of research one might do to test PCT; a book that puts a little more “flesh on the bones” of PCT research.

BN: Music to my ears! I have been hoping you would take this up. Yes!

RM: Actually, I’m not sure I know how to do it. But I’ll try. I’ll need all the help I can get.

BN: In laboratory experiments, input, output, and disturbance quantities can be precisely measured, and extraneous disturbances can be perceived and controlled to zero by the experimenter. Methodological help is also needed for naturalistic work in conditions that cannot be so precisely controlled, for practical reasons and sometimes for ethical reasons. A huge proportion of such situations involve collective control, especially where elements of the environmental feedback paths are collectively controlled. Kent’s chapter for LCS IV lays out the issues and principles brilliantly. (I’m reading part 2 now.) But methodological help is needed with research into collective control.

RM: I am available for methodological consulting; special rates for CSG members;-)

RM: If we can get a critical number of researchers doing PCT … and … providing models for other researchers to imitate, I think we will then see PCT start to have a greater influence in the life sciences.

BN: People’s interest perks up with socially significant variables. Baseball catching has greater intrinsic interest than pursuit tracking, but its grounding in the game of baseball only brings it just over the horizon of social significance. There is enormous need for understanding our participation in collective control, and for understanding social change, because there is a great deal of social change going on, and much more of it in months and years shortly to come. We know that a grasp of PCT brings with it a radical change in our perception of rewards and punishments and the nature of motivation. Such change is needed in our institutions and social norms. An understanding of collective control, and how pervasive it is, and how necessary, similarly shrinks the narcissistic fantasy called ‘independence’ to a perspective that is more modest and more capable of autonomous control. (The difference between independence and autonomy.)

RM: I agree that sexy, socially significant research could attract people to PCT. But I’m still more interested in seeing more of the “balls rolling down inclined planes” kind of research done on PCT, the kind that really got physics off the ground. I think that one of the things that has kept my experimental psychologist peers – many of whom, like my PhD adviser, are very smart and mathematically compentent – from getting into PCT research is that they don’t know what such research would even look like; they don’t know what questions to ask or how to answer those questions. They have been trained (as I was) to ask questions about the perceptual/cognitive processes that intervene between input and output. My book would have to “turn their heads” away from this perspective and show with examples that have obvious real world implications – that’s what I meant by putting “meat on the bones” – how to go about doing such research.

RM: Maybe the book will be about how to use the basic findings from the tracking task as the basis for studying more “real world” examples of behavior. Anyway, another research book, hopefully one that more clearly explains the relationship of PCT to “real world” examples of behavior, is on my “to do” list.

Best

Rick

/Bruce


Richard S. Marken

www.mindreadings.com
Author of Doing Research on Purpose.
Now available from Amazon or Barnes & Noble

On Wed, Oct 21, 2015 at 4:15 PM, Richard Marken rsmarken@gmail.com wrote:

[From Rick Marken (2015.10.21.1315)]

On Tue, Oct 20, 2015 at 2:00 PM, Bruce Nevin bnhpct@gmail.com wrote:

BN: Feedback and feedforward seem to have been reduced to synonyms for afferent and efferent, with no sense of a loop. This terminological bushwhack needs to be recognized and countered explicitly in any presentation, publication, or conversation. (Same with controlled variable vs. the several senses of ‘control variable’.)

RM: I agree. But I think the best way to “combat” this kind of misunderstanding of perceptual control to reduce the time spent countering these ideas and increase the time spent doing research (and application) based on PCT. I say this as one who is perhaps the most guilty of having spent way too much time countering these “open loop” ideas (in presentations, publications, and conversations) and far too little time doing PCT research.

RM: The question about the future of PCT made me realize that one of the big impediments to the adoption of PCT (by researchers anyway) – one that I hadn’t thought of before – is that people who might be interested in doing such research don’t really know how to do it. People trained in experimental or physiological psychology have been shown the kind of research that is done in the field; they have lots of models for what counts as a good research. But there are few such models for doing research based on PCT.

RM: PCT research is going to be very different than the kind of research that is familiar to conventional experimental/physiological psychologists. The questions a PCT researcher asks about behavior are completely different than those asked by a conventional researcher. So when a conventional psychologist gets interested in PCT he is either put off by the fact that he doesn’t know how to study it or assumes that the way you study it is the way you have been studying behavior all along and the result is the Carver/Scheier approach to PCT.

RM: DRoP (which you so kindly reviewed) is aimed at showing people how to do PCT research. But I think it might be a little too abstract, even for people who are already involved in psychological research. I think what I will have to do is write another book that is less formal and includes lots of suggestions for the kind of research one might do to test PCT; a book that puts a little more “flesh on the bones” of PCT research. If we can get a critical number of researchers doing PCT(what that number is, I don;'t know, but I’m pretty sure it’s > 3) and, thus, providing models for other researchers to imitate, I think we will then see PCT start to have a greater influence in the life sciences.

Best

Rick

There is a third sense of the term ‘internal model’, along the lines that Martin was talking about. The structure of the control hierarchy–the interconnections and weights of connections, the perceptual input functions, the reference input functions, and tje output functions–constitute a kind of model of the environment in which that control hierarchy controls successfully.

These folks seem to be trying to implement the ‘cognitive maps’ kind of notion promulgated in Cognitive Psychology: sensory data come in, are correlated with cognitive maps, ‘information processing’ takes place, and the computer in the skull issues commands to muscles.

/BN


Richard S. Marken

www.mindreadings.com
Author of Doing Research on Purpose.
Now available from Amazon or Barnes & Noble

On Sat, Sep 13, 2014 at 2:58 PM, Richard Marken rsmarken@gmail.com wrote:

[Rick Marken (201409.13.1200)]

Rupert Young (2014.09.13 13.00)–

  RY: What my question was leading to was, is the PCT structure of

reference signals ‘internal models’, as the paper authors would
recognise them?

RM: I think “internal models” can refer to two different things. One is what Adam referred to: models of the physical environment that allow “feedforward” predictions of the effect of system output on the controlled variable. This kind of internal model can improve control somewhat in a completely predictable (and, thus, artificial) environment but not so much in a real one where disturbances are upredictable. This is why, as Adam puts it succinctly “Simulations [with internal models:RM] run fine, robots worse”. There is really nothing in PCT that corresponds to this kind of “internal model”.

RM: The other kind of “internal model” corresponds to a higher level controlled perception in PCT. Although I haven’t read the Saunders “Feedforward and prosthesis control” paper carefully, my impression is that the improved gripping with vision results from the subjects’ ability to control a higher level visual variable that could not be controlled when the prosthesis was out of sight.

RM: The desire to show that control results from output calculation (feedforward) is baffling to me. But it’s there and it makes it very difficult to get people to pay attention to PCT, particularly because (as Adam also notes) "the published criticism [of the calculated output approach to control:RM] comes from the Equilibrium Point hypothesis (EP) crowd (Feldman, Latash, …) " which, as Adam politely notes “doesn’t work very well” (since it is not a control model:RM).

RM: I think what is common to both the calculated output and EP approaches to control is fear of purposiveness; both aim to “account for” control (apparent purposiveness) in terms of lineal causal laws. Unfortunately, the purposiveness of control is not apparent; it is a fact. Ergo my continued harping on the fact of control. I really think that is the big deal about PCT: PCT shows us that the behavior of living system is control (purposeful), in fact, not just in theory. PCT shows how to demonstrate this fact and how to explain it.

Best regards

Rick

Richard S. Marken, Ph.D.
Author of Doing Research on Purpose.
Now available from Amazon or Barnes & Noble