Perception of risk and uncertainty (was Re: The Hammer and Nail Example)

[From Fred Nickols (2015.11.09.1542)]

All good points, Martin. Thanks for taking the time to respond. Lots of food for thought.

Fred Nickols

···

From: Martin Taylor [mailto:mmt-csg@mmtaylor.net]
Sent: Monday, November 09, 2015 2:10 PM
To: csgnet@lists.illinois.edu
Subject: Perception of risk and uncertainty (was Re: The Hammer and Nail Example)

[Martin Taylor 2015.11.09.11.25]

[From Fred Nickols (2015.11.09.1052)]

I am watching/monitoring the traffic but that’s not the variable I’m controlling.

I think it’s a mistake to talk about “the” variable one is controlling. At the very least one is controlling variables all up and down the hierarchy, from the top to the bottom, all the time, perhaps even when sleeping. If there’s any “the” perception one controls, it may be either one’s perception of one’s self-image or one’s perception of how others perceive one (one’s “other-image”?). And one is likely to be controlling many variables at any level of the hierarchy, though the limitations of our joints and muscles limit how many we can be actively influencing at any one moment.

So I have to interpret your question as referring to one of these multiple possibilities. You assert that you are not controlling any perception of the traffic, and that’s presumably correct, since you can’t influence the traffic except possibly by waving at a cab or doing something drivers would perceive as unusual and worth looking at, such as taking off your clothes and standing on your head, or stepping into the flow (hoping that the oncoming car will brake).

I am controlling for it being safe to cross the street.

You say two things here. You want to cross the street, so you are controlling a perception of your location relative to the street, and you are controlling a predicted perception of “being safe”. What is this latter? Whatever it is, you are controlling it in imagination – planning.

When the traffic poses no risk I’ll cross.

You say you are controlling a perception of risk, which I interpret as implying that “being safe” is the reference value for the risk perception. But where can “risk” perception fit in the hierarchy? It’s a perception related to your perception of the uncertainty about the various perceptual outcomes possible if you acted to change your location, together with the imagined results of different possible actions. But if you are already controlling for perceiving yourself on the other side of the street, why aren’t you moving now? What inhibits the action component of that control system? It must be a consequence of the risk perception, mustn’t it? These questions are just another way of asking what you ask next, but doing so from a slightly different angle.

I guess I am controlling for not being struck by a car. So how would you describe the controlled variable? Is it perceived safety? Is it perceived risk? What I’m waiting to do is cross the street. Doing so changes my physical location. Am I controlling for location? I will cross when I decide it’s safe to do so. Maybe I am controlling my decision to cross.

How does one control a decision? To do so means you are perceiving the state of the decision and setting it to a reference value, which would be either “go” or “stay”. But to set a decision perception to that reference value is exactly equivalent to allowing or inhibiting the output from the control system for location perception. Why, then, control a decision perception? Or is the decision an observer’s view of an output that the observer had imagined could have been different?

Whether you are actually controlling a “struck by car” perception with a reference of nor being struck is something you may be able to intuit, but we (external observers) cannot know, because the appropriate TCV is impossible to employ other than by way of your verbal reports, which are actually the results of testing quite different controlled perceptions (e.g.perceiving the experimenter to be pleased). To employ the test directly might get you killed, which would prematurely terminate the testing without a reliable result:-)

This whole question of controlling risk and uncertainty perception is something that needs to be addressed within PCT, though so far as I can see, there’s no way to slot it into the hierarchy. We consciously perceive risk or uncertainty in many situations, and control both the magnitude of the risk (by not walking into traffic) and of uncertainty (by seeking relevant new information). We can control uncertainty about perceptions at any level of the hierarchy (not about the actual “now” perceptions, because as Bill often pointed out, there’s no uncertainty about them – they are what they are). But do we do this unconsciously, too? I assume so, but I don’t know so.

At the moment my perception of my uncertainty about this whole issue is that my uncertainty is quite high, whereas my reference value for it is quite low. I imagine that the actions that will reduce it involve both theoretical and experimental work, but of a kind that I don’t know. To paraphrase both Bill P and Donald Rumsfeld, I perceive what I perceive and that’s fact, but the problem is that I don’t perceive what I don’t perceive, and therein lies the risk.

Martin

[From
Fred Nickols (2015.11.09.1052)]

        I

am watching/monitoring the traffic but that’s not the
variable I’m controlling.

[From Rick Marken (2015.11.10.1140)]

Martin Taylor (2015.11.09.11.25)--

MT: I think it's a mistake to talk about "the" variable one is controlling.

RM: Good point. But I think it is possible to focus on one controlled variable at a time, knowing that the means used to control that variable are themselves controlled variables and that the controlled variable you are focusing on is also the means by which a higher order variable is controlled. For example, we can focus on the optical variables controlled when catching a ball and ignore the fact that the movements used to control these variables are themselves controlled variables and that the catching ia also the means of controlling for some other variable, like getting a person out in a game of baseball.

MT: This whole question of controlling risk and uncertainty perception is something that needs to be addressed within PCT, though so far as I can see, there's no way to slot it into the hierarchy.

RM: I don't know about "slotting it into the hierarchy" but I think Powers has already provided a pretty nice demonstration of how to model control of risk in his "Feedback Model for Behavior: Application to a Rat Experiment" which is reprinted on pp. 47- 59 of LCS I. The rats in that experiment are required to make a certain number of bar presses in a specified interval in order to avoid getting shocked. Rats in that situation respond at a rate that is high enough to reduce the risk of getting shocked to nearly zero. So the rats are clearly controlling their risk of getting shocked, keeping it far below the level it would be if the rats did nothing. In terms of the categories in the "Behavior is control" spreadsheet the rat's behavior in this experiment could be analyzed this way:

Behavior: Shock avoidance

Controlled Variable: Risk of getting shocked
Reference State: Zero
Means: Rate of bar pressing

Disturbance: Length of interval during which press must occur

RM: The next step is to find a model that explains this behavior -- a control model, since this behavior is clearly a process of control. The most important part of the model is developing a precise definition of the controlled variable. Powers actually tried two different definitions: "the probability of getting a shock" ,p.s, and "the rate at which shock occurs", r.s. Figure 1 shows how p.s is operationalized ; r.s is just p.s/I, where I is the duration of the interval during which a response must occur to prevent a shock.
RM: Powers doesn't mention this in the paper but p.s and r.s can be considered two different descriptions of the perception the rats are controlling when they are controlling the "risk of getting shocked". It turns out that p.s is a better definition of the controlled variable than r.s because assuming that p.s is controlled gives a better fit to the data than assuming that r.s is controlled (see Table 1. p 50).
RM: I think this is a very nice example of a PCT model of risk control as the control of the probability of a very unwanted event (a shock in this case but it could also be getting hit by a car while crossing the street) by acting in order to keep that probability at zero.
RM: I don't know where the perception of a probability fits into the hierarchy; any suggestions? But I think what's important is that this lovely "Rat Experiment" paper shows that what we see as "control of risk" can be nicely accounted for by PCT as control of the perception of the probability of unwanted events.
RM: It also shows that LCS I is a goldmine!
Best
Rick

···

--
Richard S. Marken
<https://urldefense.proofpoint.com/v2/url?u=http-3A__www.mindreadings.com&d=BQMFaQ&c=8hUWFZcy2Z-Za5rBPlktOQ&r=-dJBNItYEMOLt6aj_KjGi2LMO_Q8QB-ZzxIZIF8DGyQ&m=9AZ2JTBJBt8Ni0p7B_TxTGiffXVLTSpZVbYc1_iMfuc&s=whUACuVeFrPBHaPi0Qqs13KRgaXt5qmmQQslh46yNb4&e=>www.mindreadings.com
Author of <https://urldefense.proofpoint.com/v2/url?u=http-3A__www.amazon.com_Doing-2DResearch-2DPurpose-2DExperimental-2DPsychology_dp_0944337554_ref-3Dsr-5F1-5F1-3Fie-3DUTF8-26qid-3D1407342866-26sr-3D8-2D1-26keywords-3Ddoing-2Bresearch-2Bon-2Bpurpose&d=BQMFaQ&c=8hUWFZcy2Z-Za5rBPlktOQ&r=-dJBNItYEMOLt6aj_KjGi2LMO_Q8QB-ZzxIZIF8DGyQ&m=9AZ2JTBJBt8Ni0p7B_TxTGiffXVLTSpZVbYc1_iMfuc&s=-EvTcau8EKo8GaLaNuLYEFGv3XJVRE6S4TaY6EYwCPU&e=>Doing Research on Purpose.
Now available from Amazon or Barnes & Noble

[Martin Taylor 2015.11.10.16.27]

Yes. I had intended to say that, but on rereading my message, I see

it got lost in the writing. Thanks for pointing it out.

Yes, well remembered. I'd forgotten that one. It is very neat, and

reinforces the point that one (rats, anyway) can perceive
probability and use it in generating a reference value for some
other control system whose output affects the probability. One might
ask why the rats allowed any shock, instead of apparently setting
their reference value for shock probability to a low but non-zero
value. Since the parameters from the 8-presses-required mouse fit
the 1-press mouse, clearly the mice were not limited by their
ability to press the bar quickly. They could have avoided shock
entirely if they both made 15 presses per interval, but they didn’t.
I assume the reason is likely to be conflict between the risk
control system, which might have a reference value of zero and some
other system which tries to set a reference value of zero for the
bar pressing rate – in other words, the rats presumably don’t like
pressing the bar, but more strongly dislike getting shocked.

Perhaps it's an example of Kent McClelland's "collective control

through conflict" that he demonstrated at CSG-93, in which the
actions of conflicted control systems appear to an observer to be
the same as would have been seen if there were just one controller
with a reference value between those of the two in conflict. Here we
have the shock perception that presumably has a reference value of
no shock and whose output sets a high reference value for bar
pressing rate, conflicted with a system of some kind that has a zero
reference for bar pressing rate, resulting in a visible output of
bar pressing fast enough to avoid all but a tolerable probability of
shock.

I don't think it does. Like category perception, it can apply to

perceptions at any level of the hierarchy, and like category
perception, it depends on (from the Analyst’s viewpoint) the
possibility of things not currently being perceived. You wouldn’t
have a category “red” if everything was red, of “bird” if all
configurations were birds, or of “democrats” if all politicians were
democrats. It’s the old question “Can a fish perceive water?” when
it has experienced nothing else.

Likewise, you can't perceive a probability of X for something unless

there was a possibility of not perceiving X and perceiving something
different in its place. The rats mostly perceived “not shock” but
occasionally did perceive “shock”. I think both category and
probability perception take their inputs from the hierarchy but are
not of the hierarchy.

Yes, I think it does.

Like all Bill’s writing.

Martin
···

[From Rick Marken (2015.11.10.1140)]

            Martin Taylor

(2015.11.09.11.25)–

            MT: I think it's a mistake to talk about "the" variable

one is controlling.

          RM: Good point. But I think it is possible to focus on

one controlled variable at a time, knowing that the means
used to control that variable are themselves controlled
variables and that the controlled variable you are
focusing on is also the means by which a higher order
variable is controlled.

            MT: This whole

question of controlling risk and uncertainty perception
is something that needs to be addressed within PCT,
though so far as I can see, there’s no way to slot it
into the hierarchy.

          RM: I don't know about "slotting it into the hierarchy"

but I think Powers has already provided a pretty nice
demonstration of how to model control of risk in his
“Feedback Model for Behavior: Application to a Rat
Experiment” which is reprinted on pp. 47- 59 of LCS I.

          The rats in that experiment are

required to make a certain number of bar presses in a
specified interval in order to avoid getting shocked. Rats
in that situation respond at a rate that is high enough to
reduce the risk of getting shocked to nearly zero. So the
rats are clearly controlling their risk of getting
shocked, keeping it far below the level it would be if the
rats did nothing. …
RM: I think this is a very nice example of a PCT
model of risk control as the control of the probability
of a very unwanted event (a shock in this case but it
could also be getting hit by a car while crossing the
street) by acting in order to keep that probability at
zero.

            RM: I don't know where the perception of a

probability fits into the hierarchy; any suggestions?

            But I think what's important is that this lovely

“Rat Experiment” paper shows that what we see as
“control of risk” can be nicely accounted for by PCT as
control of the perception of the probability of unwanted
events.

RM: It also shows that LCS I is a goldmine!

[Bruce Nevin (2015.11.14.12:32 ET)]

MT: This whole question of controlling risk and uncertainty perception is something that needs to be addressed within PCT, though so far as I can see, there’s no way to slot it into the hierarchy.

RM: I don’t know about “slotting it into the hierarchy” but I think Powers has already provided a pretty nice demonstration of how to model control of risk in his “Feedback Model for Behavior: Application to a Rat Experiment” which is reprinted on pp. 47- 59 of LCS I. The rats in that experiment are required to make a certain number of bar presses in a specified interval in order to avoid getting shocked. Rats in that situation respond at a rate that is high enough to reduce the risk of getting shocked to nearly zero. So the rats are clearly controlling their risk of getting shocked, keeping it far below the level it would be if the rats did nothing.

In that article, Bill is careful to avoid saying with certitude that the rats are controlling a perception of probability or risk.

BP (p. 58): It is reasonable to suppose that the hypothesis which gives the better fit is the closer to the actual nature of qi. The present analysis suffers from the defect that the distribution curve was assumed rather than measured. If an experiment were set up to record this distribution, then it would be possible to arrive at a better definition of qi.

BP (pp. 54-55): there are many [analytical, i.e. numerical] forms similar to those chosen which serve nearly as well. The actual assumptions are few indeed: the shape for the distribution curve, and the linear proportionality assumed for the rat function.

BP (p. 59): Finally, it must be remembered that the sensory apparatus of organisms contain interpretive apparatus: the input quantity may, in fact, be a function of many sensory inputs, and may come into existence only after several stages of perceptual data processing. Even when that is the case, a feedback analysis along the lines suggested here can enable the experimenter to arrive at a reasonable approximation of the actual aspect of the environment that the organism is regulating, even when that aspect is an abstraction like density, or relative size, or a probability."

In B:CP, Bill advanced proposals as to the neuroanatomy of several kinds of perceptual functions. Here, his purpose is not anatomical, but rather polemical: his purpose is to show that “The presence of feedback makes this sort of experimentation [such as that reported in Verhave (1959)]) simply the wrong approach” (p. 57).

A major difficulty attempting to “slot into the hierarchy” a perceptual function for probability is the ubiquity of probability or risk. For any controlled perception at any level one may attempt a measure of probability of successful control.

This has a clear relation to the input function. At every level except the lowest (Intensity), the input function of a given perception combines a number of perceptual signals from lower levels. For the sake of a label, call these tributary perceptions. When there is adequate perceptual input (few or no tributary perceptions are provided by memory and imagination in absence of environmental input), control is excellent and the risk of failure to control is low. The lower the proportion of tributary perceptions that come from memory and imagination rather than from the environment, the less reliable the organism’s control through the environmental feedback function. The reason, obviously, is that control in imagination is not affected by disturbances in the environment.

So risk is inversely proportional to the richness of perceptual input from the environment, where “richness” is understood relative to the given perceptual input function. An important effect of learning (whether by problem-solving or by reorganization) is to increase the richness of environment-sourced perceptual input in the perceptual input functions affected by the learning. The rat is learning how to avoid being shocked.

BP (pp.49-50): After sufficient practice for a given setting of the interval timer and a given number[-of-presses] requirement (constant during one experiment), rats would approach some equilibrium rate of pressing. Thus a relationship was explored with the setting of the interval timer as the independent variable and the equilibrium rate of pressing as the dependent variable. Each experimental point was the average of three different four-hour averages of rate of bar-pressing. Scatter among the three determinations for a single point was on the order of one press per minute.

To grasp the degree of obfuscation of data, consider: The intervals varied between 15 seconds and 300 seconds (5 minutes). A 4-hour session of 5-minute intervals comprised 48 intervals, and 4 hours of 15-second intervals comprised 960 intervals. So between 48 and 960 rates per interval were averaged, depending on the interval length, and then for each interval length these averages were further averaged for three such 4-hour sessions. For 5-minute intervals, the rates for 144 intervals were averaged, and for 15-second intervals, rates for 2,880 intervals were averaged. Is it any wonder that for ‘control’ of an average of average rates of pressing the best estimate of qi is another statistical measure, probability?

Environmental inputs for the rat are few and slender. The rat could not perceive the requisite number of presses N as a controllable variable (8 presses in one experiment, just 1 press in the other!), and control shock by getting N presses out of the way as quickly as possible. This would not work unless it also knew when the timer reset, so that it would know to execute N presses again. Did the rat reorganize to control the equilibrium rate which just avoids being shocked? That would be analogous to adjusting one’s driving speed so as to get to all the traffic lights while they are green. But no:

BP (p. 50): The average rate of bar-pressing was always much faster than the rate actually required in order to avoid shock. the reason for this can be seen in the variations in bar-pressing rate; even with the average rate at a value fast enough to avoid shock, on some trials the random variations in rate were sufficient to delay the 8th press enough after reset of the timer to permit a shock.

Is it possible the rat occasionally slowed just enough to incur a shock in order to get more perceptual input as to the conditions under which it occurs? If you have an enemy, you want to know where it is. Is the enemy even still there? The simpler explanation is that the rats were controlling shock as well as they could while still trying to recognize some relationship between the shocks and some perceptible feature of their environment, while the actually relevant features (the timer and the required rate) were hidden from them.

Data for the process of learning culminating in an ‘average equilibrium’ rate might give some clues as to what perceptual variables the rats were controlling or trying to control. There simply is not enough data–we do not have enough perceptual input without a lot of imagined input–to control the perception that the rats were controlling a perception of probability of being shocked.

Another perceptual phenomenon that is troublesome to “slot into the hierarchy” is categorization. I have proposed for almost 20 years that categorization is a natural effect of every perceptual input function at every level, insofar any given tributary signal that the input function calls for can come from a variety of environmental sources. Any bifurcation is a member of the same category, whether that configuration is further perceived as a fork in the road, a fork or crotch in a tree, or the waist and legs of a biped. This is the basis of analogy and of important aspects of associative memory.

Bill successfully modeled Verhave’s data. Does that mean that the rat is controlling shock probability? A successful model demonstrates the fact of control, but, so far, gives only a broad outline of the neuroanatomy of control.

···

On Tue, Nov 10, 2015 at 2:38 PM, Richard Marken rsmarken@gmail.com wrote:

[From Rick Marken (2015.11.10.1140)]

Martin Taylor (2015.11.09.11.25)–

MT: I think it's a mistake to talk about "the" variable one is

controlling.

RM: Good point. But I think it is possible to focus on one controlled variable at a time, knowing that the means used to control that variable are themselves controlled variables and that the controlled variable you are focusing on is also the means by which a higher order variable is controlled. For example, we can focus on the optical variables controlled when catching a ball and ignore the fact that the movements used to control these variables are themselves controlled variables and that the catching ia also the means of controlling for some other variable, like getting a person out in a game of baseball.

MT: This whole question of controlling risk and uncertainty perception

is something that needs to be addressed within PCT, though so far as
I can see, there’s no way to slot it into the hierarchy.

RM: I don’t know about “slotting it into the hierarchy” but I think Powers has already provided a pretty nice demonstration of how to model control of risk in his “Feedback Model for Behavior: Application to a Rat Experiment” which is reprinted on pp. 47- 59 of LCS I. The rats in that experiment are required to make a certain number of bar presses in a specified interval in order to avoid getting shocked. Rats in that situation respond at a rate that is high enough to reduce the risk of getting shocked to nearly zero. So the rats are clearly controlling their risk of getting shocked, keeping it far below the level it would be if the rats did nothing. In terms of the categories in the “Behavior is control” spreadsheet the rat’s behavior in this experiment could be analyzed this way:

Behavior: Shock avoidance

Controlled Variable: Risk of getting shocked

Reference State: Zero

Means: Rate of bar pressing

Disturbance: Length of interval during which press must occur

RM: The next step is to find a model that explains this behavior – a control model, since this behavior is clearly a process of control. The most important part of the model is developing a precise definition of the controlled variable. Powers actually tried two different definitions: “the probability of getting a shock” ,p.s, and “the rate at which shock occurs”, r.s. Figure 1 shows how p.s is operationalized ; r.s is just p.s/I, where I is the duration of the interval during which a response must occur to prevent a shock.

RM: Powers doesn’t mention this in the paper but p.s and r.s can be considered two different descriptions of the perception the rats are controlling when they are controlling the “risk of getting shocked”. It turns out that p.s is a better definition of the controlled variable than r.s because assuming that p.s is controlled gives a better fit to the data than assuming that r.s is controlled (see Table 1. p 50).

RM: I think this is a very nice example of a PCT model of risk control as the control of the probability of a very unwanted event (a shock in this case but it could also be getting hit by a car while crossing the street) by acting in order to keep that probability at zero.

RM: I don’t know where the perception of a probability fits into the hierarchy; any suggestions? But I think what’s important is that this lovely “Rat Experiment” paper shows that what we see as “control of risk” can be nicely accounted for by PCT as control of the perception of the probability of unwanted events.

RM: It also shows that LCS I is a goldmine!

Best

Rick

Richard S. Marken

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

[From Rick Marken (2015.11.15.1350)]

···

Bruce Nevin (2015.11.14.12:32 ET)

RM: …Powers has already provided a pretty nice demonstration of how to model control of risk in his “Feedback Model for Behavior: Application to a Rat Experiment” which is reprinted on pp. 47- 59 of LCS I. The rats in that experiment are required to make a certain number of bar presses in a specified interval in order to avoid getting shocked. Rats in that situation respond at a rate that is high enough to reduce the risk of getting shocked to nearly zero. So the rats are clearly controlling their risk of getting shocked, keeping it far below the level it would be if the rats did nothing.

BN: In that article, Bill is careful to avoid saying with certitude that the rats are controlling a perception of probability or risk.

RM: We never say “with certitude” that we have identified a controlled variable. But we can say which hypothesis about the controlled variable is better than others. As you quote Bill in the paper:

BP (p. 58): It is reasonable to suppose that the hypothesis which gives the better fit is the closer to the actual nature of qi.

RM: Both hypotheses about the controlled variable in the experiment, “probability of shock”, p.s, and “rate of shock”, p.r, give very good fits to the data but p.s does better than p.r so p.s is considered the current best estimate of the actual controlled variable,q.i.

RM Bill goes on to describe how you might be able to get an even better estimate of the controlled variable:

BP: The present analysis suffers from the defect that the distribution curve was assumed rather than measured. If an experiment were set up to record this distribution, then it would be possible to arrive at a better definition of qi.

RM: What Bill is saying here is that it would have been better if the researchers had measured the actual number of shocks obtained after each interval so that a more empirical estimate of q.i could have been used in the modeling. They didn’t measure that because this research was conducted without an understanding of the nature of the behavior of closed loop control systems. That is, the research was done without an understanding of control so they didn’t even think of getting a measure of the possible controlled variable. Billl was trying to make a silk purse (a control theory analysis of behavior) out of a sow’s ear (a conventional psychological experiment) and I think he did it!

RM: So while Bill is careful to avoid saying “with certitude” that he has discovered what the organisms in this experiment are controlling he does say that:

BP (p. 59):… a feedback analysis along the lines suggested here can enable the experimenter to arrive at a reasonable approximation of the actual aspect of the environment [emphasis mine - RM] that the organism is regulating, even when that aspect is an abstraction like density, or relative size, or a probability."

RM: And isn’t it interesting that Bill refers to the variable the organism is controlling – the controlled variable – as an “aspect of the environment”! Indeed, p.s, the probability of a shock, the perception that is hypothetically being controlled, is an aspect of the organism’s (and observer’s) environment; that’s why Bill was able to measure it!

BN: In B:CP, Bill advanced proposals as to the neuroanatomy of several kinds of perceptual functions. Here, his purpose is not anatomical, but rather polemical: his purpose is to show that “The presence of feedback makes this sort of experimentation [such as that reported in Verhave (1959)]) simply the wrong approach” (p. 57).

RM: Neuroanatomy has nothing to do with it. The reason Bill said that “The presence of feedback makes this sort of experimentation simply the wrong approach” is because it is not based on an understanding of behavior as control. There is no awareness that the rats were controlling something about the shocks. So the design of the experiment was not aimed at testing for controlled variables. There was no measurement of the variance of the hypothetical controlled variable (actual shock rate). The shocks, though rare once the rat learned how to control their occurrence, were likely to have set off reorganization, increasing the noisiness of the data. This just wasn’t an experiment designed to study control; it was designed to study behavior as an output selected by consequences.

BN: A major difficulty attempting to “slot into the hierarchy” a perceptual function for probability is the ubiquity of probability or risk. For any controlled perception at any level one may attempt a measure of probability of successful control.

RM: The goal of testing for controlled variables is to figure out the variables around which a particular behavior is organized; it is not to “slot the variable into the hierarchy”.

BP (pp.49-50): After sufficient practice for a given setting of the interval timer and a given number[-of-presses] requirement (constant during one experiment), rats would approach some equilibrium rate of pressing. Thus a relationship was explored with the setting of the interval timer as the independent variable and the equilibrium rate of pressing as the dependent variable. Each experimental point was the average of three different four-hour averages of rate of bar-pressing. Scatter among the three determinations for a single point was on the order of one press per minute.

BN: To grasp the degree of obfuscation of data, consider: The intervals varied between 15 seconds and 300 seconds (5 minutes). A 4-hour session of 5-minute intervals comprised 48 intervals, and 4 hours of 15-second intervals comprised 960 intervals. So between 48 and 960 rates per interval were averaged, depending on the interval length, and then for each interval length these averages were further averaged for three such 4-hour sessions. For 5-minute intervals, the rates for 144 intervals were averaged, and for 15-second intervals, rates for 2,880 intervals were averaged. Is it any wonder that for ‘control’ of an average of average rates of pressing the best estimate of qi is another statistical measure, probability?

RM: It’s not pressing rate that is being tested to see if it’s a controlled variable, it’s the shock probability (or rate). But it is not clear to me why an average of average pressing rates would result in the best estimate of the controlled variable being a probability, p.s, rather than a rate, p.r. Maybe you could show me the proof. I was actually a tad surprised by the result. I thought average rate, p.r, would give the best fit.

BN: Bill successfully modeled Verhave’s data. Does that mean that the rat is controlling shock probability?

RM: No, but it means that they are more likely controlling p.s than p.r. So the next step in research should be designing the study correctly, to test for the controlled variable, and see if we can get a better bead on what the rat is controlling.

BN: A successful model demonstrates the fact of control,

RM: Yes, and in the process it gives a very good estimate of the nature of the controlled variable.

BN: but, so far, gives only a broad outline of the neuroanatomy of control.

RM: I don’t think it gives any outline of the neurophysiology at all. Figuring out the nature of the perceptual functions that transform the controlled aspect of the environment into a perception is a whole different ballgame. Certainly one worth playing but pretty much irrelevant to the conclusions about the controlled variable based on the test for the controlled variable.

Best

Rick


Richard S. Marken

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

[Bruce Nevin (2015.11.17.09:10)]

I agree with every point you make here, except to emphasize the role that PCT can play in guiding neuroscience research. I’m with Boris on the need for anatomical plausibility. Certainly it would be helpful to my interest in a PCT account of word dependencies in language if there are neural structures that compute probabilities as perceptual signals. I’m suggesting that we get that already as a property of perceptual input functions.

BN: Is it any wonder that for ‘control’ of an average of average rates of pressing the best estimate of qi is another statistical measure, probability?

RM: It’s not pressing rate that is being tested to see if it’s a controlled variable, it’s the shock probability (or rate).

Yes, the rate of pressing is not what is being tested. But what is the rat able to perceive? And what kind of prior training did the rat experience? Did they start with a low number of presses to avert a shock and work up to 8? Seems implausible to me that you could just pitch a naive rat into this torture chamber and wait for it to just hit on the expedient of pressing the lever more and more often until it got up to 8 presses in the (current) interval between shocks. Was it already familiar with pressing a lever to get food or water? The process of learning, whatever it was, must have involved a perception of pressing the lever repeatedly as lower-level means of higher-level shock avoidance. With so many unknowns, a probabilistic account of the CV is perhaps unavoidable. A silk purse tour de force, yes, and greatly to Bill’s credit, but the hairiness of the sow’s ear still shows.

···

On Sun, Nov 15, 2015 at 4:52 PM, Richard Marken rsmarken@gmail.com wrote:

[From Rick Marken (2015.11.15.1350)]

Bruce Nevin (2015.11.14.12:32 ET)

RM: …Powers has already provided a pretty nice demonstration of how to model control of risk in his “Feedback Model for Behavior: Application to a Rat Experiment” which is reprinted on pp. 47- 59 of LCS I. The rats in that experiment are required to make a certain number of bar presses in a specified interval in order to avoid getting shocked. Rats in that situation respond at a rate that is high enough to reduce the risk of getting shocked to nearly zero. So the rats are clearly controlling their risk of getting shocked, keeping it far below the level it would be if the rats did nothing.

BN: In that article, Bill is careful to avoid saying with certitude that the rats are controlling a perception of probability or risk.

RM: We never say “with certitude” that we have identified a controlled variable. But we can say which hypothesis about the controlled variable is better than others. As you quote Bill in the paper:

BP (p. 58): It is reasonable to suppose that the hypothesis which gives the better fit is the closer to the actual nature of qi.

RM: Both hypotheses about the controlled variable in the experiment, “probability of shock”, p.s, and “rate of shock”, p.r, give very good fits to the data but p.s does better than p.r so p.s is considered the current best estimate of the actual controlled variable,q.i.

RM Bill goes on to describe how you might be able to get an even better estimate of the controlled variable:

BP: The present analysis suffers from the defect that the distribution curve was assumed rather than measured. If an experiment were set up to record this distribution, then it would be possible to arrive at a better definition of qi.

RM: What Bill is saying here is that it would have been better if the researchers had measured the actual number of shocks obtained after each interval so that a more empirical estimate of q.i could have been used in the modeling. They didn’t measure that because this research was conducted without an understanding of the nature of the behavior of closed loop control systems. That is, the research was done without an understanding of control so they didn’t even think of getting a measure of the possible controlled variable. Billl was trying to make a silk purse (a control theory analysis of behavior) out of a sow’s ear (a conventional psychological experiment) and I think he did it!

RM: So while Bill is careful to avoid saying “with certitude” that he has discovered what the organisms in this experiment are controlling he does say that:

BP (p. 59):… a feedback analysis along the lines suggested here can enable the experimenter to arrive at a reasonable approximation of the actual aspect of the environment [emphasis mine - RM] that the organism is regulating, even when that aspect is an abstraction like density, or relative size, or a probability."

RM: And isn’t it interesting that Bill refers to the variable the organism is controlling – the controlled variable – as an “aspect of the environment”! Indeed, p.s, the probability of a shock, the perception that is hypothetically being controlled, is an aspect of the organism’s (and observer’s) environment; that’s why Bill was able to measure it!

BN: In B:CP, Bill advanced proposals as to the neuroanatomy of several kinds of perceptual functions. Here, his purpose is not anatomical, but rather polemical: his purpose is to show that “The presence of feedback makes this sort of experimentation [such as that reported in Verhave (1959)]) simply the wrong approach” (p. 57).

RM: Neuroanatomy has nothing to do with it. The reason Bill said that “The presence of feedback makes this sort of experimentation simply the wrong approach” is because it is not based on an understanding of behavior as control. There is no awareness that the rats were controlling something about the shocks. So the design of the experiment was not aimed at testing for controlled variables. There was no measurement of the variance of the hypothetical controlled variable (actual shock rate). The shocks, though rare once the rat learned how to control their occurrence, were likely to have set off reorganization, increasing the noisiness of the data. This just wasn’t an experiment designed to study control; it was designed to study behavior as an output selected by consequences.

BN: A major difficulty attempting to “slot into the hierarchy” a perceptual function for probability is the ubiquity of probability or risk. For any controlled perception at any level one may attempt a measure of probability of successful control.

RM: The goal of testing for controlled variables is to figure out the variables around which a particular behavior is organized; it is not to “slot the variable into the hierarchy”.

BP (pp.49-50): After sufficient practice for a given setting of the interval timer and a given number[-of-presses] requirement (constant during one experiment), rats would approach some equilibrium rate of pressing. Thus a relationship was explored with the setting of the interval timer as the independent variable and the equilibrium rate of pressing as the dependent variable. Each experimental point was the average of three different four-hour averages of rate of bar-pressing. Scatter among the three determinations for a single point was on the order of one press per minute.

BN: To grasp the degree of obfuscation of data, consider: The intervals varied between 15 seconds and 300 seconds (5 minutes). A 4-hour session of 5-minute intervals comprised 48 intervals, and 4 hours of 15-second intervals comprised 960 intervals. So between 48 and 960 rates per interval were averaged, depending on the interval length, and then for each interval length these averages were further averaged for three such 4-hour sessions. For 5-minute intervals, the rates for 144 intervals were averaged, and for 15-second intervals, rates for 2,880 intervals were averaged. Is it any wonder that for ‘control’ of an average of average rates of pressing the best estimate of qi is another statistical measure, probability?

RM: It’s not pressing rate that is being tested to see if it’s a controlled variable, it’s the shock probability (or rate). But it is not clear to me why an average of average pressing rates would result in the best estimate of the controlled variable being a probability, p.s, rather than a rate, p.r. Maybe you could show me the proof. I was actually a tad surprised by the result. I thought average rate, p.r, would give the best fit.

BN: Bill successfully modeled Verhave’s data. Does that mean that the rat is controlling shock probability?

RM: No, but it means that they are more likely controlling p.s than p.r. So the next step in research should be designing the study correctly, to test for the controlled variable, and see if we can get a better bead on what the rat is controlling.

BN: A successful model demonstrates the fact of control,

RM: Yes, and in the process it gives a very good estimate of the nature of the controlled variable.

BN: but, so far, gives only a broad outline of the neuroanatomy of control.

RM: I don’t think it gives any outline of the neurophysiology at all. Figuring out the nature of the perceptual functions that transform the controlled aspect of the environment into a perception is a whole different ballgame. Certainly one worth playing but pretty much irrelevant to the conclusions about the controlled variable based on the test for the controlled variable.

Best

Rick


Richard S. Marken

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

[From Rick Marken (2015.11.18.1405)]

···

Bruce Nevin (2015.11.17.09:10)–

BN: I agree with every point you make here, except to emphasize the role that PCT can play in guiding neuroscience research. I’m with Boris on the need for anatomical plausibility. Certainly it would be helpful to my interest in a PCT account of word dependencies in language if there are neural structures that compute probabilities as perceptual signals. I’m suggesting that we get that already as a property of perceptual input functions.

RM: I agree that PCT can – and should – play a role in guiding neuroscience research. I just don’t see how it can guide neuroscience research aimed at determining how perceptual functions work. PCT research could suggest the kind of perceptual functions the neuro-scientists should be looking for. But I don’t see how it could help guiding research into the physiological implementation of these functions. But I can imagine PCT guiding research on how control systems are implemented in the NS, in terms of the functional connections between perceptual, comparator and output functions. And, of course, the PCT model should remain consistent with what is already known about how the NS works (which Bill did a pretty great job of doing in B:CP).

RM: What I object to is arguments that suggest that the neurophysiology should guide PCT. That seems to be what is going on in cognitive neuroscience these days. The neurophysiology is used to “explain” the behavioral phenomena that should be explained by a functional model. I once attended a talk on cognitive neuroscience where the presenter said that conflicts were explained by activity in a certain brain region because that brain region lit up in an fMRI while a person was in conflict. To me, that’s like explaining how a spreadsheet program works by pointing out that a particular location on the processor chip is most active when a spreadsheet is being run.

RM: Neurophysiology should constrain and be guided by PCT; but it shouldn’t (and can’t) guide PCT.

Best

Rick


Richard S. Marken

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