[From Fred Nickols (02.22.2014.0830 EST)]

I changed the subject line to keep this thread separate from the LCS III discussion.

A postscript: I have always been troubled by statements like the one at the end of Frans’ post: “Behavior *is* the control of perception.” I have always preferred “Behavior serves to control perception.” I’ve been hanging around long to get that but, for me and millions upon millions of others (my estimate) behavior is “the activity of the organism.”

I’m not being testy; I’m simply trying to avoid unnecessary conflict.

Fred

## ···

**From:** Fred Nickols [mailto:fred@NICKOLS.US]

**Sent:** Saturday, February 22, 2014 8:19 AM

**To:** CSGNET@LISTSERV.ILLINOIS.EDU

**Subject:** Re: LCS III: Review of Chapter 2

[Fred Nickols (02.22.2014.0817 EST)]

Frans:

Many thanks for this. I think you are correct. I am especially glad they used the term “target” as it fits with my Target Model of Human Behavior and Performance which is based on PCT.

Thanks again.

Fred Nickols

**From:** Frans Plooij [mailto:fplooij@KIDDYGROUP.COM]

**Sent:** Saturday, February 22, 2014 7:25 AM

**To:** CSGNET@LISTSERV.ILLINOIS.EDU

**Subject:** Re: LCS III: Review of Chapter 2

Dear all,

has anybody seen the following article of February 18:

Shanechi, M. M., Hu, R. C. & Williams, Z. M. A cortical-spinal prosthesis for targeted limb movement in paralysed primate avatars. *Nat Commun* **5**, 3237, doi:10.1038/ncomms4237 (2014).

The abstract is as follows:

Motor paralysis is among the most disabling aspects of injury to the central nervous system. Here we develop and test a target-based cortical-spinal neural prosthesis that employs neural activity recorded from premotor neurons to control limb movements in functionally paralysed primate avatars. Given the complexity by which muscle contractions are naturally controlled, we approach the problem of eliciting goal-directed limb movement in paralysed animals by focusing on the intended targets of movement rather than their intermediate trajectories. We then match this information in real-time with spinal cord and muscle stimulation parameters that produce free planar limb movements to those intended target locations. We demonstrate that both the decoded activities of premotor populations and their adaptive responses can be used, after brief training, to effectively direct an avatar’s limb to distinct targets variably displayed on a screen. These findings advance the future possibility of reconstituting targeted limb movement in paralysed subjects.

As they are focusing on the intended targets of movements instead of their intermediate trajectories, isn’t this the first physiological proof of this kind that behavior is the control of perception?

Best, Frans

Dr. Frans X. Plooij

Director

International Research-institute on Infant Studies (IRIS)

Zijpendaalseweg 73

6814 CE Arnhem

The Netherlands

Mobile: +31 6 460 888 20

Email: fplooij@kiddygroup.com

Tel.: +31 26 389 4841

Fax: +31 26 389 4493

Op 21 feb. 2014, om 20:04 heeft Richard Marken rsmarken@GMAIL.COM het volgende geschreven:

[From Rick Marken (2014.02.21.1100)]

David Goldstein disputed my criticism of the “restriction of range” explanation of the low correlation between disturbance and controlled variable in a control system. Here’s the relevant part of the discussion.

DG: > 4.Consider Bill’s statement on page 25: "A second way to find the

controlled variable is to look for the minimum correlation between the

disturbance and the aspect of the ball being influenced by the disturbance."

Can you explain this in terms of the properties of the correlation

statistic? (hint: what happens to the size of a correlation coefficient when

the range of one of the variables is restricted?)

Rm: Oops, this is not a result of restriction of range (which is a reduction in the size of the correlation between X and Y that results from looking at the correlation between X and Y for only a subrange (just as just the lower half of he range) of the X or Y variable. The low correlation between disturbance and “the aspect of the ball being influenced by the disturbance” which is the controlled perceptual variable (CV), results from the fact that the subject’s outputs are preventing the disturbance from moving the CV from it’s reference state. The range of the CV is being restricted by the actions of the system, not by the person computing the correlation coefficient. So the low correlation between disturbance and CV is a result of restriction of the range of the CV but this is quite different that the restriction of range that results in a reduced correlation coefficient. In the case of control, only the range of the CV is being “restricted” by the actions of the control system; in the statistical restriction of range case, both the range of both the X and Y variables is being restricted when you restrict the range of one variable (or the other).

RM: It certainly looks like the low correlation between disturbance (d) and controlled variable (c) is an example of “restriction of range”. In statistics, “restriction of range” means that the correlation between two variables, like c and d, goes down as the range of one of the variables is restricted; that is,when only a subset of the entire range of one of the variables (and the values of the other variable that are associated with that subrange) is included in the analysis.

This sounds like what is going on in control. In the control situation the range of one variable, c, would be equal to the range of the another, d, if the control system weren’t controlling c. And controlling c definitely reduces the range of variation of c. With no control, the range of c is the same as the range of d and the correlation between d and c is 1.0; with good control, the range of d is still the same as it was without control but the range of c is considerably restricted and, sure enough, the correlation between d and c goes down, ultimately to 0 when control is perfect.

But the reduced correlation between c and d when there is control is not a result of the restriction of range of c; it’s a result of the active cancellation of the effect of d on c by the output of a control system. I’ve cobbled together a spreadsheet (attached) to try to illustrate this point. Using a simulated control system I’ve demonstrated how you get a small correlation between d and c when the range of c is reduced by the actions of a control system and a high (indeed, perfect) correlation between d and c when the range of c is reduced by just multiplying the the c values by fraction.

The main results are in the upper right corner of the sheet. “d range” is the range of the disturbance - 200 in this case. “c range” is the range of c, the variable controlled by the control system (12.54, pretty good control); “c’ range” is the range of c values computed as .0001*d (.02). “r d-c” is the correlation between d and c (.19). This is the low correlation we always see when we correlate d and c from a tracking task done by a relative skilled controller."r d-c’ "is the correlation between d and c’ (1.0). Note that even though the range of c’ is much smaller than the range of c the correlation between d and c’ is much higher than that between d and c.

So it’s not the reduced (“restricted”) range of c relative to the range of d that is responsible for for the low correlation between d and c in a control task. It’s the fact that c = d-o, with o nearly exactly equal to d.

I’ve also included an example of the effect of “restriction of range” on correlation as it is meant in statistics. I looked at the correlation between two variables, X and Y; the correlation between X and Y (rX-Y) was .84. The range of the X variable (X range) was 0-200. I then selected for further analysis only the X scores (and associated Y scores) that feel in the range 0 - 100. In other words, I restricted the X,Y pairs to be included in the analysis to those with X scores in the lower half of the X range of scores. The X and Y scores from this subrange are called X’and Y’ and the resulting correlation between these scores (r X’-Y’) is only ,6. So by including scores from only a restricted range of one of the variables involved in a correlation analysis (X in this case) you end up with a reduced correlation between the values of the variables in this restricted range. This is the “restriction of range” effect described in statistics texts.

So hopefully you can see that the statistical “restriction of range” reduction in the correlation between two variables is nothing like the reduction in the correlation between disturbance and controlled variable that is seen when control “restricts the range” of variation of the controlled variable.

Best

Rick

–

Richard S. Marken PhD

www.mindreadings.com

The only thing that will redeem mankind is cooperation.

– Bertrand Russel