[From Bruce Abbott (950724.1200 EST)]

Rick Marken (950724.0800) --

Bruce Abbott (950721.1100 EST)

Even so, I think you'll agree that the fits are impressive.

Yes, it's quite impressive -- if you ignore the fact that it is trivial;-)

First convert a variable, y, to 3600/y; call that variable yc. Then do a

regression of x on yc. Then use the regression equation to find predicted

values of yc; call these yc'. Then "predict" the values of y (y') from

3600/yc'. Since yc = 3600/y then y = 3600/yc, so it is not really THAT

surprising that y' (3600/yc') is as good a predictor of y (3600/yc) as yc'

is of yc.

So what does it mean?

"Nothing" would be my first guess. But a behaviorist would have a field day,

as Bruce illustrates with his usual keen wit.

Guess again. If I did what you say I did above, you would be right to

criticize it as circular. Fortunately (or unfortunately, depending on your

point of view), you have completely misrepresented my approach. I suggest

you go back to my posting and carefully reanalyze it. Surely at some point

in your graduate education they taught you about nonlinear transformations

of data.

As a starting point, try graphing the data as originally presented (response

rate as a function of reinforcement rate, for each ratio requirement).

Next, replot the data, giving reinforcement rate as a function of ratio

requirement. Finally, plot seconds/reinforcement as a function of ratio

requirement. Let us know what you discover. I think you'll find that the

conclusion is far from trivial.

Last I heard you were proposing to work on the operant PCT model at the

meeting. Did you make any progress?

I would like to hear more about the meeting. Is anyone planning to provide

an overview on CSG-L?

Regards,

Bruce