lane of traffic

[From Bruce Nevin (2017.08.26.20:39 ET)]

I think you need a different example of behavior to model with a comparator having two outputs. I do not believe we control a perception “car in center of lane” to keep the car in the center of the lane. I believe we control to avoid the edges of the lane, just as we avoid obstacles.

When I see an approaching vehicle pulling toward the center while passing some bicyclists riding along the edge of the road, I pull away from the center of the lane toward the right margin so as to give him or her more room.

When I go around a curve to the left, I approach the center line because my line of sight to the right margin is obscured by the hood and right fender. If I approach the right margin on that curve I feel less in control. Partly I think it’s because centrifugal force is pulling me that way, and I resist it. There is another reason, too. On a straightaway, I am controlling my line of motion relative to the road margins a considerable distance ahead. Visual features seem to approach relatively slowly at that distance, and I have plenty of time to correct deviations. I remember that when I was learning to drive, I controlled distance between the edges of the car and points much closer to the car on the edges of the road. At that distance, visual features seem to approach quite fast. On this curve to the left I am constrained to see visual features which are much closer than those that I control on a straightaway. I have less leisure to correct deviations, and subjectively I feel less securely in control.

Conversely, when I go around a curve to the right, the center line on the outside of the curve is comparatively much more visible than the right edge was in the above case, because I’m sitting on the left side of the car. I tend to hug the inside of that curve too, but not so closely as I hug the inside of a left-hand curve.

You may suppose that people ‘cut corners’ because it’s shorter and faster. Behavior is not significantly different when the driver is going slowly to e.g. an appointment that he dreads, and therefore is not controlling “get there faster”.

There is a reason that roads are equipped with a center line that divides the two lanes. It sets a boundary. Oncoming cars belong on the other side of that boundary, and I belong on this side. Now imagine that you are driving in a country where there is no dividing line. Instead, there is a center line in each lane. Your task as a driver is to keep your car centered on that line, and oncoming cars are to do the same. Between you and oncoming traffic, with a combined speed over 100 mph, is unmarked blacktop. Are you comfortable with that? I’m not. I want that boundary.

I think these anecdotes support my perception that we control avoidance of lane margins in the same way that we control avoidance of obstacles, and that the appearance of ‘car in center of lane’ (true only on a straightaway, mind you) is a consequence of that.

So what other experience are you going to model with a comparator having two outputs?

···

/Bruce

[From Rick Marken (2017.08.27.1110)]

···

Bruce Nevin (2017.08.26.20:39 ET)

BN: I think you need a different example of behavior to model with a comparator having two outputs. I do not believe we control a perception “car in center of lane” to keep the car in the center of the lane. I believe we control to avoid the edges of the lane, just as we avoid obstacles.

RM: I’m not sure who the “you” is that you are replying to but I can say that the two comparator model has nothing to do with the type of behavior (controlling) that is being modeled; rather, it is a way to implement a bi-directional control system (one in which the controlled variable can be brought to the reference state whether it is greater than or less than the reference specification) in the nervous system, which carries only positive neural signals.Â

BestÂ

Rick

Â

When I see an approaching vehicle pulling toward the center while passing some bicyclists riding along the edge of the road, I pull away from the center of the lane toward the right margin so as to give him or her more room.Â

When I go around a curve to the left, I approach the center line because my line of sight to the right margin is obscured by the hood and right fender. If I approach the right margin on that curve I feel less in control. Partly I think it’s because centrifugal force is pulling me that way, and I resist it. There is another reason, too. On a straightaway, I am controlling my line of motion relative to the road margins a considerable distance ahead. Visual features seem to approach relatively slowly at that distance, and I have plenty of time to correct deviations. I remember that when I was learning to drive, I controlled distance between the edges of the car and points much closer to the car on the edges of the road. At that distance, visual features seem to approach quite fast. On this curve to the left I am constrained to see visual features which are much closer than those that I control on a straightaway. I have less leisure to correct deviations, and subjectively I feel less securely in control.

Conversely, when I go around a curve to the right, the center line on the outside of the curve is comparatively much more visible than the right edge was in the above case, because I’m sitting on the left side of the car. I tend to hug the inside of that curve too, but not so closely as I hug the inside of a left-hand curve.

You may suppose that people ‘cut corners’ because it’s shorter and faster. Behavior is not significantly different when the driver is going slowly to e.g. an appointment that he dreads, and therefore is not controlling “get there faster”.Â

There is a reason that roads are equipped with a center line that divides the two lanes. It sets a boundary. Oncoming cars belong on the other side of that boundary, and I belong on this side. Now imagine that you are driving in a country where  there is no dividing line. Instead, there is a center line in each lane. Your task as a driver is to keep your car centered on that line, and oncoming cars are to do the same. Between you and oncoming traffic, with a combined speed over 100 mph, is unmarked blacktop. Are you comfortable with that? I’m not. I want that boundary.

I think these anecdotes support my perception that we control avoidance of lane margins in the same way that we control avoidance of obstacles, and that the appearance of ‘car in center of lane’ (true only on a straightaway, mind you) is a consequence of that.

So what other experience are you going to model with a comparator having two outputs?

/Bruce

Richard S. MarkenÂ

"Perfection is achieved not when you have nothing more to add, but when you
have nothing left to take away.�
                --Antoine de Saint-Exupery

[Martin Taylor 2017.08.27.08.55]

[From Bruce Nevin (2017.08.26.20:39 ET)]

        I think you need a different example of behavior to model

with a comparator having two outputs. I do not believe we
control a perception “car in center of lane” to keep the car
in the center of the lane. I believe we control to avoid the
edges of the lane, just as we avoid obstacles.

I quite agree (and with the elided part of your quoted message), and

that was also what Fred said, if I now interpret him correctly. I
used the example only because it had been brought up, and the
context was controlling for being in the centre of the lane, a
normal tracking control problem. You argue that this particular
situation is not actually controlling for being in the centre of the
lane, but is a double avoidance problem, and I agree. The thread,
however, was about how to treat negative values of the error in an
ordinary control loop, and Fred had offered an example in which
negative perceptions might occur.


So what other experience are you going to model with a
comparator having two outputs?

/Bruce

Assuming from the previous context that you are really asking about

what experiences require control of perceptions that can be positive
or negative, here’s a very short list that suggests a large number
of possibilities. Controlling for putting a picture at a certain
height on a wall. Controlling for having an arrow hit the bull’s eye
on a target, Controlling any relationship perception, whether or not
a boundary is implied, such as the centre-line of the road or going
too much into debt. Word pairs such as “above-below”, “left-right”,
“before-after”, “more than-less than”, and the like signal
possibilities for negative values of some perception.

Answering your literal question about two separate error signals as

output from the comparator, I would say “all perceptions that can be
modelled with the canonical control loop”. Normal two-way control
has the possibility that the perception might be either greater or
less than the reference value. Since it is impossible for the number
of firings of a neuron to be negative, the only way for the
comparator to send a negative value to the output function is to
send a positive signal that terminates on inhibitory synapses. Since
AFAIK a synapse is either permanently excitatory or permanently
inhibitory, I infer that this requires separate excitatory and
inhibitory outputs from the comparator, created by two separate
neurons or complexes of neurons.

The added complexity of my newer diagram in the thread "The

hierarchy (was Re: Dealing with the limitation of only positive
neural signals)", as opposed to the earlier “top-left quadrant of
Figure 4.6” is because of the need to accommodate signal paths that
represent negative possible values for the perception and the
reference.

Returning to your comments about controlling to avoid being too far

left or too far right, we have not often discussed controlling to
avoid some reference value. We ordinarily discuss controlling to
approach reference values. Controlling to avoid is a more difficult
topic. Bill, in such demos as the crowd, finessed the issue by
controlling the inverse of distance, but that’s not a general
solution.

"Controlling to avoid" could be an interesting thread on its own.

What could be the controlled “inverse” perceptual value of a
standard “approach the reference” kind of loop, for “Controlling to
avoid Doris seeing me with Belinda”, or “Controlling not to see the
countermanding flag signal” (Lord Nelson, with the action being to
put the telescope to his blind eye), avoiding annoying the person
you are with, avoiding the taste of pineapple (my personal problem
with fruit salads over many decades), avoiding falling into an old
well while dropping stones into it to hear the splash, avoiding
giving Judy the book on Ancient Trigonometry? The list goes on, and
few of these cases can be resolved by treating a distance perception
as an inverse “proximity”. Many are based on yes-no perceptions.
Doris does or does not see me with Belinda. Do I fall into the well
when I look down into it or do I stay safely dropping stones down
it? Do I give Brian the Ancient Trigonometry book, do I sell the
book to Judy rather than give it, do I give Judy a different book
rather than that one? There’s no inverse, except linguistically.
“Perceive not-this” as a reference does not have the specificity of
“Perceive this”. It’s an interesting possible topic.

Martin

[From Bruce Abbott (2017.08.27. 1505 EDT)]

[From Bruce Nevin (2017.08.26.20:39 ET)]

BN: I think you need a different example of behavior to model with a comparator having two outputs. I do not believe we control a perception “car in center of lane” to keep the car in the center of the lane. I believe we control to avoid the edges of the lane, just as we avoid obstacles

Another problem with this example is that, even with controlling for keeping the car in the center of its lane, the control system needed to do this is relatively complex. The steering wheel position does not determine the position of the car relative to the reference position. Together with the car’s forward speed and the adhesion of the tires to the road, it determines the rate of turning. To control the car’s position we could employ a two-level hierarchical control system in which the top level controls position and the bottom level controls turning rate. Error in the position-control system determines the reference for turning rate. The output of the turning-rate control system sets the steering-wheel angle. If the car drifts, say, to the right of the reference position (lane center), an error appears which sets the turning-rate reference to produce a turn whose rate is proportional to the size of the position error and in the direction that reduces this error (in this case, to left). The error in the turning-rate system determines the steering wheel angle.

In a mechanical system the angle of the steering wheel might be varied by means of an electric motor (open loop) or a servomotor (closed loop). In humans it is varied by changing the reference levels for muscle tensions/lengths and/or joint angles of the limbs used to turn the steering wheel. (This adds additional layers to the hierarchy described above).

And all this complexity emerges even before we attempt to deal with control systems that can act only in one direction.

Bruce A.

[From Bruce Abbott (2017.08.27. 1505 EDT)]

···

[From Bruce Nevin (2017.08.26.20:39 ET)]

            BN: I think you need a different

example of behavior to model with a comparator having
two outputs. I do not believe we control a perception
“car in center of lane” to keep the car in the center of
the lane. I believe we control to avoid the edges of the
lane, just as we avoid obstacles

            BA: Another problem with this example

is that, even with controlling for keeping the car in
the center of its lane, the control system needed to do
this is relatively complex. The steering wheel position
does not determine the position of the car relative to
the reference position. Together with the car’s forward
speed and the adhesion of the tires to the road, it
determines the rate of turning. …

[From Bruce Nevin (2017.08.27.17:35 ET)]

Rick Marken (2017.08.27.1110)–

RM: I’m not sure who the “you” is that you are replying to

Generically referring to “you” plural who had been engaging in the conversation. I could as easily have said “we”, except that I hadn’t participated in the conversation. The reason I hadn’t was that my disagreement about the example was irrelevant to the interesting discussion about the control circuitry. That’s why I waited (well, I’ve been busy with other things, too), and that’s why I put a new subject line on this thread.

Martin Taylor 2017.08.27.08.55 –

Thanks for the many examples. In all cases where it could as easily be modeled either way, Ockham recommends the simpler model.

The ‘muscle tone’ of opposed muscles for limb position or tracking might be an example? To maintain position both muscles are tensed.

The general case is where a perception is controlled by varying a number of lower-level perceptions, with strengths depending upon the ‘direction’ in which the input perception deviates from the reference. Pointing at a target in x-y space can deviate from the target in any direction around 360 degrees, a given muscle pulls in one direction, and disturbance in any given direction is opposed by some combination of tensions in several muscles resulting in a net vector of force on the limb.

Bruce Abbott (2017.08.27. 1505 EDT) –

···

On Sun, Aug 27, 2017 at 12:06 PM, Bruce Abbott bbabbott@frontier.com wrote:

[From Bruce Abbott (2017.08.27. 1505 EDT)]

[From Bruce Nevin (2017.08.26.20:39 ET)]

BN: I think you need a different example of behavior to model with a comparator having two outputs. I do not believe we control a perception “car in center of lane” to keep the car in the center of the lane. I believe we control to avoid the edges of the lane, just as we avoid obstacles

Another problem with this example is that, even with controlling for keeping the car in the center of its lane, the control system needed to do this is relatively complex. The steering wheel position does not determine the position of the car relative to the reference position. Together with the car’s forward speed and the adhesion of the tires to the road, it determines the rate of turning. To control the car’s position we could employ a two-level hierarchical control system in which the top level controls position and the bottom level controls turning rate. Error in the position-control system determines the reference for turning rate. The output of the turning-rate control system sets the steering-wheel angle. If the car drifts, say, to the right of the reference position (lane center), an error appears which sets the turning-rate reference to produce a turn whose rate is proportional to the size of the position error and in the direction that reduces this error (in this case, to left). The error in the turning-rate system determines the steering wheel angle.

In a mechanical system the angle of the steering wheel might be varied by means of an electric motor (open loop) or a servomotor (closed loop). In humans it is varied by changing the reference levels for muscle tensions/lengths and/or joint angles of the limbs used to turn the steering wheel. (This adds additional layers to the hierarchy described above).

And all this complexity emerges even before we attempt to deal with control systems that can act only in one direction.

Bruce A.

[From Bruce Abbott (2017.08.27.1815 EDT)]

Martin Taylor 2017.08.27.15.48 –

[From Bruce Abbott (2017.08.27. 1505 EDT)]

[From Bruce Nevin (2017.08.26.20:39 ET)]

BN: I think you need a different example of behavior to model with a comparator having two outputs. I do not believe we control a perception “car in center of lane” to keep the car in the center of the lane. I believe we control to avoid the edges of the lane, just as we avoid obstacles

BA: Another problem with this example is that, even with controlling for keeping the car in the center of its lane, the control system needed to do this is relatively complex. The steering wheel position does not determine the position of the car relative to the reference position. Together with the car’s forward speed and the adhesion of the tires to the road, it determines the rate of turning. …

MT: Do you really think controlling a perception of where the car is in its lane is especially complex when compared to other perceptions we control, such as perceiving the time and space structure involved in arranging a party or generating a message such as this?

BA: No. I think the example of controlling a perception of where the car is in it lane is a poor choice of example, if the goal is to explain clearly how the nervous system can exert bidirectional control using neural signals that can only be positive. I provided another reason (the complexity of this system) to agree with Bruce (and with Fred) that another (simpler) example would be better suited to this purpose.

MT: All that has been at issue in this ever-changing thread is whether there exist cases in which perceptions vary from one side to the other either of a reference value (in the case of the comparator output) or of some other perception (in the case of a relationship).

BA: The existence of such cases is beyond dispute; thus I don’t think that this has been at issue at all; what has been at issue is how bidirectional control can be achieved in a system that can generate only positive values (neural firing rates).

MT: The examples show that such cases do exist, and in the case of the comparator are almost universal. Only when there is a hard limit on achievable perceptual values and the reference value is up against that limit (or beyond it) is there no possibility of the perception being both above and below the reference value. Only then can a single sided comparator produce effective outputs.

MT: How control is actually accomplished at lower levels is always complex, and is usually irrelevant when considering the control of one perception. It is irrelevant to the present discussion, however the thread subject line changes.

BA: An example that has not been mentioned thus far of bidirectional control using unidirectional signals is control of bodily temperature. When body temperature exceeds its setpoint, neural error signals activate the sweat glands and dilate capillaries near the surface of the skin, allowing bodily heat to escape more efficiently to the atmosphere through conduction and evaporative cooling. When body temperature falls below its setpoint a different set of neural error signals activate muscle shivering (generating heat) and capillaries near the surface of the skin constrict (reducing loss of heat through the skin). The neural error signals are positive in both cases and are routed via different neurons to the appropriate output mechanisms.

Bruce

[From Erling Jorgensen (2017.08.28 1255 EDT)]

Martin Taylor 2017.08.27.08.55

Hi Martin,

EJ: I’m still confused about your assertion that the “true” values of either references or perceptions can be negative in the nervous system. You state it correctly in this portion of this post:

MT: Normal two-way control has the possibility that the perception might be either greater or less than the reference value. Since it is impossible for the number of firings of a neuron to be negative, the only way for the comparator to send a negative value to the output function is to send a positive signal that terminates on inhibitory synapses.

EJ: However, you then go back to asserting that negative values need to be represented as signals, apart from what is happening via the inhibitory synapses:

MT: The added complexity of my newer diagram in the thread “The hierarchy (was Re: Dealing with the limitation of only positive neural signals)”, as opposed to the earlier “top-left quadrant of Figure 4.6” is because of the need to accommodate signal paths that represent negative possible values for the perception and the reference.

EJ: I’m reproducing that portion of your improved diagram for clarity’s sake, but I still take issue with it:

[Martin Taylor 2017.08.26.09.46]

EJ: I agree that the (-r+p) output needs to be routed through an inhibitory synapse at the next step in the equation, to preserve negative feedback control. But having that represented as -r in a composite diagram like this ends up misrepresenting the algebra, as if this “true” negative signal can then be reversed by the inhibition (as in the upper of these two units) into a net positive signal. That’s counting the negative signs twice. And it arises, to my way of thinking, because of erroneously labeling a signal path as negative.

EJ: I still say we only need that initial upper left quadrant of your diagram (without the need for the headings about positive value, because all long-range neural signals are positive), reproduced here:

EJ: You go on to discuss these signals in terms of relationship perceptions, but I believe there are better ways to model a function representing a relationship, than adding minus signs to signal paths.

All the best,

Erling

···

Disclaimer: This message is intended only for the use of the individual or entity to which it is addressed, and may contain information that is privileged, confidential and exempt from disclosure under applicable law. If the reader of this message is not the intended recipient, or the employer or agent responsible for delivering the message to the intended recipient, you are hereby notified that any dissemination, distribution or copying of this communication is strictly prohibited. If you have received this communication in error, please notify the sender immediately by telephone and delete the material from your computer. Thank you for your cooperation.

[Martin Taylor 2017.08.28.13.48]

[From Erling Jorgensen (2017.08.28 1255 EDT)]

Martin Taylor 2017.08.27.08.55

Hi Martin,

      EJ:  I'm still confused about your assertion that the

“true” values of either references or perceptions can be
negative in the nervous system. You state it correctly in
this portion of this post:

      >MT: Normal two-way control has the possibility that the

perception might be either greater or less than the reference
value. Since it is impossible for the number of firings of a
neuron to be negative, the only way for the comparator to send
a negative value to the output function is to send a positive
signal that terminates on inhibitory synapses.

      EJ:  However, you then go back to asserting that negative

values need to be represented as signals, apart from what is
happening via the inhibitory synapses:

The synapses are inhibitory BECAUSE the positive signals represent

negative values. There’s no “apart from”.

      >MT: The added complexity of my newer diagram in the

thread “The hierarchy (was Re: Dealing with the limitation of
only positive neural signals)”, as opposed to the earlier
“top-left quadrant of Figure 4.6” is because of the need to
accommodate signal paths that represent negative possible
values for the perception and the reference.

      EJ:  I'm reproducing that portion of your improved diagram

for clarity’s sake, but I still take issue with it:

[Martin Taylor 2017.08.26.09.46]

      EJ:  I agree that the (-r+p) output needs to be routed

through an inhibitory synapse at the next step in the
equation, to preserve negative feedback control. But having
that represented as -r in a composite diagram like this ends
up misrepresenting the algebra, as if this “true” negative
signal can then be reversed by the inhibition (as in the upper
of these two units) into a net positive signal.

??? I have no idea what you are saying here. There's no "reversal by

inhibition" anywhere. Inhibition just is treated as though it
subtracts the positive value on the signal path from whatever other
input there may be to the unit to which it synapses

      That's counting the negative signs twice.  And it arises,

to my way of thinking, because of erroneously labeling a
signal path as negative.

How "erroneous"? What would you prefer the label to be? All it means

is “this is the path that is used when a lower-level process
produces a number that algebraically should be negative”, but I’m
not going to write all that on the figure. I think a simple “-” is
quite adequate. You don’t, so I presume you have a better
suggestion. What? I suppose I could write “p-” rather than “-p”, but
I prefer the latter, because it seems more clearly to suggest that
the value on the line is the negative of the negative value.

      EJ:  I still say we only need that initial upper left

quadrant of your diagram (without the need for the headings
about positive value, because all long-range neural signals
are positive), reproduced here:

      EJ:  You go on to discuss these signals in terms of

relationship perceptions, but I believe there are better ways
to model a function representing a relationship, than adding
minus signs to signal paths.

Let's just do a little analysis here. Suppose the perceptual

function of this control unit produces a relationship that the
external analyst would label “Above-Below”. When X is above Y, the
algebraic result of the perceptual function is positive, and that is
sent to the comparator as a positive value. This “upper-left
quadrant” diagram produces an inhibitory input (subtraction) for the
upper unit and an excitatory input (addition) for the lower unit.
When X is below Y the algebraic result of the perceptual function is
negative, but that can’t be sent as a negative value. It must be
sent as a positive value, the absolute value of the difference. If
it sent over the same path, it will produce the same result for both
locations of Y. So it must be sent over a different path in some
form. The simplest form in which to send it is its absolute value,
and that is what I have been assuming is sent.

Give these some numbers to make it more concrete. For the upper-left

quadrant as sufficient in itself: r=3, p=|±4|. The “r” value
indicates that X is supposed to be above Y by 3 units. If X is above
Y by 4 units (p=4), r-p = -1, yielding an output of 1 from the lower
half-comparator and zero from the higher, which is correct. Y is one
unit too low. If Y is higher than X by 4 units (p=-4, |p|=4), we get
the same result, whereas the external analyst who can see both X and
Y can see that Y is now seven units too high, being 4 units above X
when the reference is for it to be 3 units below X.

Now consider the "double-double" diagram above with separate paths

labelled “p”, “-p”, “r” and “-r”. This time, when X is above Y
there is a positive value on the path labelled “p”, and zero on the
path marked “-p”. When Y is above X, there is a positive value on
the path labelled “-p” and zero on the path marked “p”. Using the
same numbers as before, r = 3, “p” = 4, and “-p” = 0 when Y is low,
and r = 3, “p” = 0, and “-p” = 4 when Y is high. The outputs from
the two half-comparators are the same as before when “p” > 0, but
when “p”=0 and “-p” = 4, they are not. Now the upper half-comparator
gives a correct output of 7 and the lower gives zero.

You can do the same kind of analysis with the reference location for

Y being either above or below X (“-r” positive, “r” zero in the
latter case). You do need all four quadrants of my original Figure
4.6. All the connections from that figure are shown together in the
new “double-double” figure above.

···
However, you say you think there are better ways of representing a

relationship than by having a perceptual function that produces
positive values on just one of two signal paths that represent the
different directions in which the two related values might differ.
I’d be interested in how else it might be done. I can imagine
providing two different perceptual functions, one for each direction
of difference, but that would still result in a +p and a -p signal
path, both of which would have to be compared with the same
reference value, which might be of either effective sign (effective
means terminating on excitatory or inhibitory synapses).

[Not relevant for this discussion, but possibly relevant later, the

“double-double” figure can be generalized to provide an avoidance
comparator function. Possibly in a different thread, not here.]

Martin

Martin

[From Erling Jorgensen (2017.08.28 1702 EDT)]

[Martin Taylor 2017.08.28.13.48]

Erling Jorgensen (2017.08.28 1255 EDT)

[This refers to the double-double diagram in your post, which is not reproduced here.]

EJ: I agree that the (-r+p) output needs to be routed through an inhibitory synapse at the next step in the equation, to preserve negative feedback control. But having that represented as -r in a composite diagram like this ends up misrepresenting the algebra, as if this “true” negative signal can then be reversed by the inhibition (as in the upper of these two units) into a net positive signal.

MT: ??? I have no idea what you are saying here. There’s no “reversal by inhibition” anywhere. Inhibition just is treated as though it subtracts the positive value on the signal path from whatever other input there may be to the unit to which it synapses

EJ: That’s counting the negative signs twice. And it arises, to my way of thinking, because of erroneously labeling a signal path as negative.

EJ: The “reversal by inhibition” that I referred to was happening on the upper of the two units: There is a -r being routed via an inhibitory circle. If all the signals are positive, then the output portion from this input should simply be -r. But it’s not! Combining it with the perception, you have the output as (r-p).

EJ: You have something comparable happening in the lower of the two units, except it is coming via the path labeled -p. When this positive value enters via the inhibitory circle, the output portion should still be -p. But you have the output (combined with an inhibitory reference input) as (-r+p). That’s counting the minus signs twice, despite your assertions to the contrary.

All the best,

Erling

···

Disclaimer: This message is intended only for the use of the individual or entity to which it is addressed, and may contain information that is privileged, confidential and exempt from disclosure under applicable law. If the reader of this message is not the intended recipient, or the employer or agent responsible for delivering the message to the intended recipient, you are hereby notified that any dissemination, distribution or copying of this communication is strictly prohibited. If you have received this communication in error, please notify the sender immediately by telephone and delete the material from your computer. Thank you for your cooperation.

[Martin Taylor 2017.08.28.17.39]

[From Erling Jorgensen (2017.08.28 1702 EDT)]

[Martin Taylor 2017.08.28.13.48]

Erling Jorgensen (2017.08.28 1255 EDT)

      [This refers to the double-double diagram in your post,

which is not reproduced here.]

      >>EJ: I agree that the (-r+p) output needs to be

routed through an inhibitory synapse at the next step in the
equation, to preserve negative feedback control. But having
that represented as -r in a composite diagram like this ends
up misrepresenting the algebra, as if this “true” negative
signal can then be reversed by the inhibition (as in the upper
of these two units) into a net positive signal.

      >MT: ??? I have no idea what you are saying here.

There’s no “reversal by inhibition” anywhere. Inhibition just
is treated as though it subtracts the positive value on the
signal path from whatever other input there may be to the unit
to which it synapses

      >>EJ: That's counting the negative signs twice. And

it arises, to my way of thinking, because of erroneously
labeling a signal path as negative.

      EJ:  The "reversal by inhibition" that I referred to was

happening on the upper of the two units: There is a -r being
routed via an inhibitory circle. If all the signals are
positive, then the output portion from this input should
simply be -r. But it’s not! Combining it with the
perception, you have the output as (r-p).

      EJ:  You have something comparable happening in the lower

of the two units, except it is coming via the path labeled
-p. When this positive value enters via the inhibitory
circle, the output portion should still be -p. But you have
the output (combined with an inhibitory reference input) as
(-r+p). That’s counting the minus signs twice, despite your
assertions to the contrary.

Erling, I don't know what you are misunderstanding, but it's

something quite deep. Did you follow the numerical example and
explanation in my message?

Here's the figure again, showing numbers worked out for all four

combinations of positive and negative perception and reference
rather than just the two I worked out in my last message, using the
same numbers (±3 and ±4). “p” and “-p” are the two outputs of the
perceptual function of this control unit, “r” and “-r” are provided
from the higher level.

Remember, r or p positive means X above Y, and all signal (axon)

values are non-negative. So the top-right panel illustrates the case
that the higher-level control unit wants X three units below Y,
while the perception is that X is 4 units above Y. The error (r-p)
is -7, which is indicated by the “7” on the lower output and zero on
the upper output. The restriction to non-negative signal values
doesn’t prohibit the two half-comparators from performing
subtractions. In the Figure, they subtract the value at the
inhibitory input (indicated by a circle) from the excitatory input
(indicated by an arrowhead).

![ikjfcjdheohdmfdc.jpg|1463x950](upload://kwMEpERLXyDfoVNupa18bJnajfZ.jpeg)

The top-left corresponds to the top-left quadrant of the original

Figure 4.6, and the others to the same respective quadrants in the
original figure (unless I switched top-right with bottom-left). I
think you will find the arithmetic to be correct. If there’s still a
problem, I guess you will tell me.

Martin

[From Rick Marken (2017.08.28.1605)

···

Bruce Nevin (2017.08.26.20:39 ET)–

BN: I think you need a different example of behavior to model with a comparator having two outputs. I do not believe we control a perception “car in center of lane” to keep the car in the center of the lane. I believe we control to avoid the edges of the lane, just as we avoid obstacles.

RM: Just two quick points: 1) We are not very good at telling what variables we are actually controlling when we carry out various behaviors. I would certainly not have guessed that I am controlling the vertical optical velocity and lateral optical displacement of the visual image of a baseball when I’m trying to catch it. You may, indeed, be controlling for avoiding the edges of the lane when you are driving… But you may be controlling for something else, such as what driver’s ed teachers say you should be controlling for; keeping the center of the car aligned with a point slightly ahead of you in the center of the lane (between the two lines that are the edges of the lane). Determining what perceptions an organism is actually controlling when it is carrying out a certain behavior is what PCT research is all about. And 2) Some folks in silicon valley are apparently doing a pretty good job of designing self driving cars that can (among other things) keep themselves centered in the lane. Understanding what perceptual variables these cars are controlling might give hints about what variables people control when they drive cars and also about how the systems that control these variables are organized. This might suggest possibilities for how the nervous system does it.

BestÂ

Rick

When I see an approaching vehicle pulling toward the center while passing some bicyclists riding along the edge of the road, I pull away from the center of the lane toward the right margin so as to give him or her more room.Â

When I go around a curve to the left, I approach the center line because my line of sight to the right margin is obscured by the hood and right fender. If I approach the right margin on that curve I feel less in control. Partly I think it’s because centrifugal force is pulling me that way, and I resist it. There is another reason, too. On a straightaway, I am controlling my line of motion relative to the road margins a considerable distance ahead. Visual features seem to approach relatively slowly at that distance, and I have plenty of time to correct deviations. I remember that when I was learning to drive, I controlled distance between the edges of the car and points much closer to the car on the edges of the road. At that distance, visual features seem to approach quite fast. On this curve to the left I am constrained to see visual features which are much closer than those that I control on a straightaway. I have less leisure to correct deviations, and subjectively I feel less securely in control.

Conversely, when I go around a curve to the right, the center line on the outside of the curve is comparatively much more visible than the right edge was in the above case, because I’m sitting on the left side of the car. I tend to hug the inside of that curve too, but not so closely as I hug the inside of a left-hand curve.

You may suppose that people ‘cut corners’ because it’s shorter and faster. Behavior is not significantly different when the driver is going slowly to e.g. an appointment that he dreads, and therefore is not controlling “get there faster”.Â

There is a reason that roads are equipped with a center line that divides the two lanes. It sets a boundary. Oncoming cars belong on the other side of that boundary, and I belong on this side. Now imagine that you are driving in a country where  there is no dividing line. Instead, there is a center line in each lane. Your task as a driver is to keep your car centered on that line, and oncoming cars are to do the same. Between you and oncoming traffic, with a combined speed over 100 mph, is unmarked blacktop. Are you comfortable with that? I’m not. I want that boundary.

I think these anecdotes support my perception that we control avoidance of lane margins in the same way that we control avoidance of obstacles, and that the appearance of ‘car in center of lane’ (true only on a straightaway, mind you) is a consequence of that.

So what other experience are you going to model with a comparator having two outputs?

/Bruce


Richard S. MarkenÂ

"Perfection is achieved not when you have nothing more to add, but when you
have nothing left to take away.�
                --Antoine de Saint-Exupery