# How does the experiment do it?

**URL:** <http://discourse.iapct.org/t/how-does-the-experiment-do-it/5318>\
**Category:** CSG1996\
**Created:** [August 4, 1996, 6:59pm UTC](http://discourse.iapct.org/t/how-does-the-experiment-do-it/5318 "1996-08-04T18:59:08Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![Richard\_Marken](http://discourse.iapct.org/letter_avatar_proxy/v4/letter/r/9fc348/32.png) [@Richard\_Marken](http://discourse.iapct.org/u/Richard_Marken)\
**Post date:** [August 4, 1996, 6:59pm UTC](http://discourse.iapct.org/t/how-does-the-experiment-do-it/5318/1 "1996-08-04T18:59:08Z")

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[From Rick Marken (960804.1200)]

Hans Blom (960803) --

> This leads to the following proposal: Implement a cursor tracking task based  
> on the above "world" equation. In each trial, r is kept constant. In each  
> new trial, r has a new (random) value. This can be implemented by keeping r  
> constant for e.g. 50 time intervals of 0.1 seconds; this is a trial. The  
> action a will, I assume, stabilize within that period. After 50 time  
> intervals, a new r is presented for, again, 50 time intervals.

Hooray. A real experimental proposal! This sounds fine. But I have a  
couple of questions before I set it up.

First, I think of r as something inside the subject; it sounds to me like  
your r is a "target" on the screen. Is this correct? If so, then the  
experiment you describe is one where the target in a tracking task moves  
to a new (randomly selected) location every 5 seconds. The connection  
between the subject's action (a, which I take to be a one-dimension  
measure of mouse position) and cursor position (p) is x + y \* a. I presume  
that the values of x and y would be limited so that the subject can  
generate a value of a that brings p to the target position (r) on every  
trial. Is that correct?

> In a first experiment, x and y are kept constant throughout all trials.  
> If learning is present, tracking will improve over time. If not, then not.

How do we measure "tracking"? I suspect that the subject will be able to  
make p = r on every trial. The time to bring p to r will depend largely on  
how far the new r (visible target) is from the prior r. So how do you  
measure "tracking" so that you can see an improvement over trials (if  
improvement occurs) despite random variations in r?

> A second experiment could be a sequence of the above multi-trial experiments,  
> with new values of x and y in each element of the sequence. We expect control  
> to deteriorate when \>new values of x and y are installed, but only if a  
> "theory" has been built.

How do you measure "control" in order to see whether or not it "deteriorates"?  
If you measure control as RMS deviation of p from r over time then, again,  
this measure of control will depend on how far the new r is from the prior  
r; it will also depend on the nature of the changes in x and y; if y changes  
sign, for example, the sign of the feedback loop changes; this will produce  
a brief deterioration of subject performance (as shown in Marken and  
Powers "Mind Readings, p 109); and it will produce failure of the simple,  
single loop PCT model (there must be another loop to detect the positive  
feedback effect of the change in the sign of the loop gain in the "tracking"  
model).

> Would this be a set of experiments that could decide whether we build  
> internal models? If not, what would you suggest?

I think this set of experiments could decide whether or not we need a model  
(like yours) that builds internal models. If your "model based" control  
model mimics the data even slightly better than a PCT model, I would readily  
admit that you have provided evidence that people build "internal models".  
If, however, we can account for the results of this experiment using a  
simple PCT model, and we cannot improve our account of the data using  
your "model based" model, would you agree that there is no evidence of  
"model based" control in this situation? I bet I can predict (based on  
PCT, no less) the result of \_that\_ aspect of the experiment;-)

Best

Rick