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- Date: Fri, 8 Jan 1993 20:27:00 GMT
- Sender: "Control Systems Group Network (CSGnet)" <CSG-L@UIUCVMD.BITNET>
- From: Hortideas Publishing <0004972767@MCIMAIL.COM>
- Subject: Is that a fly swatter?
- Lines: 68
-
- From Greg Williams (920108 - 2)
-
- >Rick Marken (930108.0900)
-
- >>My point is that you have not reached
- >>that goal in predicting cursor position.
-
- >After reading this I got out my HyperCard conflict stack and
- >did a tracking run and model run (400 data points each) with a
- >low conflict and got a correlation between subject and model
- >mouse movements of .996831 -- the correlation with cursor
- >movements would be lower but at least we seem to have part of
- >a true science. I repeated this with a slightly higher level
- >of conflict and got a sublject/model correlation of .993398. Still
- >in the "true science" range. Higher conflicts will take us
- >well below .99 (to .98 maybe?) suggesting that there is something
- >to be learned there.
-
- If "true science" is your aim, why not compute the subject/model correlation
- for the INTEGRAL of the handle movement? That should get you within spittin'
- distance of 1.0! But if you want to be sobered a bit, use your model to
- compute the correlation between the modeled derivative of handle position and
- the derivative of the subject's handle position. That might even be lower than
- the cursor-prediction correlation.
-
- >How about H = K * integral(C - T)? (K is a constant to be adjusted for best
- >fit to the data by running the simulation with the model in it). That's
- >just a first cut, of course, since it doesn't predict the cursor position
- >well enough for "true science."
-
- >I think the idea, now, is to use your S-R model in a real tracking
- >situation. As I understand the challenge, you are to derive an S-R
- >model (like your equation above) from your observation of the
- >relationship between S (cursor) and H (handle movement). Bill
- >apparently sent you that data. I'd throw in the disturbance too -- I
- >don't think it's an unfair advantage for you at all -- in many experiments
- >you CAN see the disturbance (or the cause thereof) even if the subject can't.
- >So I would suggest that Bill give you D, C and H from a tracking task. Based
- >on that data, you come up with an S-R model that generates H based on what
- >the subject can see (C and T).
-
- We're back to what is and is not an "S-R" model. If I can't fit parameters
- with the model taking the place of the subject with the loop closed, I won't
- be able to get reasonable values for K. If fitting parameters with the loop
- closed makes the model above into a PCT model, rather than an S-R model, why
- is that? As required, the model has the form H = f(C,T). Input-output. But if
- I try to adjust K to make H follow the data, GIVEN THE FIXED C data, my "best"
- K will not be best when the loop is closed. By adjusting K with C NOT fixed,
- and the (given; it is just the difference between the other two givens)
- disturbance operative, the K WILL be best for OTHER disturbances, too.
-
- >Your model must then be tested by seeing if it can do what the subject
- >does -- control the cursor in a new situation. So your model must be
- >"run" (this could be cone analytically but it's easier with a computer
- >simulation) with a new disturbance -- to see if it generates the H that
- >controls the cursor (as the subject would).
-
- I would go beyond that, and try to change the FORM of the model to predict C
- better, too. Maybe even get the subject-model cursor correlation up to the
- "True Science" range... of course, that might not be possible, given noise in
- the subject. My challenge to you and Bill would then be to make an underlying
- generative model for the noise which results in a better s-m C correlation
- than is possible with behaviorist "models" which contain no underlying
- hypotheticals.
-
- As ever,
-
- Greg
-