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- Date: Thu, 7 Jan 1993 13:49:00 GMT
- Sender: "Control Systems Group Network (CSGnet)" <CSG-L@UIUCVMD.BITNET>
- From: Hortideas Publishing <0004972767@MCIMAIL.COM>
- Subject: Abandoning the .9999999+ standard?
- Lines: 109
-
- From Greg Williams (930107)
-
- >Bill Powers (930106.2100)
-
- >Control theory is more akin to classical physics, which deals
- >with a continuous macroscopic world.
-
- As I've said before, Newton was the Skinner of his day: "Hypotheses non
- fingo!" I think we must admit that he guessed right that his trying to make a
- generative model for gravity -- and then being able to test it -- would have
- been fruitless. It isn't so clear that Skinner's claim that, giving the
- current state-of-the-art (between the Thirties and the Eighties) in
- physiology, making generative models of behavior is misguided. We don't yet
- have 20-20 hindsight. At any rate, future historians will be able to judge,
- since the "cognitive movement" (which includes PCT) has called Skinner's hand.
-
- >I'm reminded of the
- >quote that Rick (or was it Tom) came up with, in which Skinner
- >admitted that it would be better to understand how the insides of
- >the organism work. He was really battling against people who
- >tossed off glib and question-begging explanations in terms of
- >traits and tendencies, and of course I'm with him all the way,
- >there. Unfortunately, he persuaded a lot of people that
- >successful models of the insides were millenia off, and not worth
- >thinking about.
-
- I found that quote, and I still have considerable sympathy for Skinner's
- pragmatism and humility. The question really boils down to whether, at a given
- time, generative models work better FOR THE PURPOSES OF THE INVESTIGATORS than
- do descriptive "models." Even in the long run, generative models simply might
- be too complicated to actually make, or you might run into chaos (hair-
- trigger) problems.
-
- >It's not worthwhile trying to make a model fit every anomaly.
-
- There goes the PCT standard of "accept nothing less 99.9999999...
- correlations." Right out the window. The high correlation between PCT-model-
- predicted handle movements and actual handle movements begins to look less
- spectacular, doesn't it? In principle, underlying generative models are more
- complete than descriptions at the level of the phenomena. But in practice, the
- former might not be able to predict better than the latter, due to the
- complexity of the underlying mechanisms and/or hair-trigger situations.
-
- >Well, I think I do, but if you want to put your model where your
- >mouth is, I will pay attention. Of course I expect it to be a
- >real S-R model -- no feedback allowed!
-
- I think you better be explicit about what you mean by "real S-R model." The
- kind of model a behaviorist would make is one which is a function of
- observables in the external world; in this case, cursor position and velocity,
- target position, and handle position and velocity. He/she would curve-fit with
- parametric variation a function relating cursor position and velocity and
- target position to handle position and velocity through time. Is that OK? If
- this is not a "real" S-R "model" (or at least a "real" "model" AT THE LEVEL OF
- THE OBSERVABLE PHENOMENA) because there is a feedback connection in the
- computer from the handle to the cursor, then you are right, I can't come up
- with a "real" "model." I can only come up with a mathematical description
- relating observable inputs and outputs, which, I contend, can be as predictive
- as the most predictive PCT model. In fact, as the behaviorist and the PCTer
- both "zero in" on the most predictive models, I think the mathematics will
- converge in all but perhaps one respect: the PCT model might include an
- underlying generative model for the "noise," while the behaviorist model will
- use probability formulae DESCRIBING the noise as observed. It isn't clear,
- given current knowledge of the nervous system, whether generative models for
- such "noise" can be more predictive than descriptive "models." I do agree, of
- course, that an underlying generative model is needed to EXPLAIN the "noise."
- But explaining and predicting are two different things, as Newton knew so long
- ago.
-
- >>It seems to me that a large number of models (PCT and S-R, each
- >>differing from the others in parameters and/or basic forms) can
- >>produce equally high correlations between actual handle
- >>position and model-predicted handle position, because the
- >>moment-to-moment differences in the predictions of the various
- >>models get "washed out" in computing those correlations.
-
- >In one sense this is certainly true, at least of PCT models.
-
- Then you go through a long list of red herrings. What I meant to say is that
- various PCT models such as proportional, proportional-integral, and
- proportional-integral-derivative, with various nonlinearities and various
- parameters, and various descriptive "models" with various functions and
- parameters, ALL give about equally high correlations between predicted handle
- positions and actual handle positions, but all do NOT give equally high
- correlations between predicted cursor positions and actual cursor positions.
-
- >But I deny that any S-R model will be able to predict the
- >behavior of the handle and the cursor with any interesting degree
- >of accuracy. It's up to you to show that I'm wrong, and you're
- >not going to accomplish that with words.
-
- It will save me a lot of trouble if I know in advance that a descriptive
- function which includes a subtraction of cursor position from target position
- won't count for you as a "real" descriptive "model." Two different
- distinctions are being interwoven here, and they need separating: feedback vs.
- non-feedback (S-R, I take it) models, and underlying generative models vs.
- descriptive "models" at the level of the observable phenomena (behaviorist
- functional "models"). I believe that the PCT models used to predict tracking
- can be interpreted also as functional "models" -- the choice of interpretation
- (underlying generative model or function "model") depends only on whether one
- envisions a reference signal for target position inside the organism (thus
- explaining why cursor position is subtracted from target position) or one
- simply notes that prediction is good if there is such a subtraction in the
- function relating "input" to "output" at each moment and doesn't care about
- explaining why.
-
- As ever,
-
- Greg
-