The way most non-pct researchers use the term “perception” is not the same either. Surely “prediction”, to everybody, implies using expectation about things that have not yet happened as part of the current set of perceptions that affect control. How far ahead those predictions go depends on the distribution of probable states x milliseconds, hours, years, or mega-years in the future. In simple tracking, the transport lag sets the minimum prediction time to be used, even in simple position control.
What limits the accuracy of prediction for, say, simple pursuit tracking, is the autocorrelation function of the disturbance, the variance accounted for by knowledge of the current value of a variable. The variance unaccounted for is the limit to prediction accuracy x time-units in the future, counting from the time of the most recent observation. Of course, the autocorrelation function itself cannot be determined without prior observations, just as the velocity and acceleration need prior observations for their determination.
In an experiment, however, the experimenter who designed the disturbance noise knows the autocorrelation function, so can be sure of the actual limits of prediction possible to an ideal tracker. It depends on the equivalent white noise bandwidth. For a sine-wave disturbance, for example, the equivalent bandwidth from the experimenter’s point of view is zero, and the ideal would be able to predict infinitely far into the future. With such a sine-save disturbance, any failure of a tracker in practice must include the inability of the perceiving apparatus to determine the equivalent disturbance bandwidth. The “failure to predict” noise of inability to use the experimenter-constrained bandwidth should be included along with other noise sources around the control loop when analyzing deviations from ideal tracking performance.
Now, perhaps we can extrapolate this to vastly different time scales, and recognize that it is in the everyday sense of “prediction” that astronomers tell us that in (If I remember correctly) 5 billion years, the Andromeda Galaxy will collide with our Milky Way. For the observations that lead to such a prediction, the disturbances are slow, with autocorrelation functions stretching billions of years into the future (so far as astronomers can determine).
Not all disturbances can be reduced to an equivalent white noise. For example, in most countries an election is a source of disturbance to the perceived structure of the Government. In some countries, such as the USA, it is almost certain there will be no federal election in the next few days after one was completed, or in the next few months, but in the few days around exactly two and exactly four years after one election, another is almost certain to occur. That disturbance is highly predictable in its timing, but its magnitude and direction is not. They are the only uncertainty in predicting how the structure of the Government may change. In other countries, the time of the next election is less precisely predictable (i.e. that an election will be held during a specified week is near zero for any week for a while after an election event, then rises until it become near unity by the legally limited life of the Government).
Maybe I have made the point. When the disturbance statistics are not rhythmic, velocity and acceleration are reasonable proxies for predictability, and controlling them as well as position in the kind of hierarchy sometimes used by Powers in his later simulations can lead to improved tracking performance. The work with Powers on his own tracking performance that I mentioned demonstrated that he could not have been tracking position alone, but that he could have been using at least velocity-based prediction, and maybe acceleration as well.
There is, of course no guarantee that living control systems use any of these variables (position, velocity, and acceleration) in pure form. Most nerves, AFAIK, seem to change their firing rate right after a change to their input, and then trend back toward their resting state. To me, this suggests that some combination of position, velocity, and acceleration, is what is reported by a “Neural current”, not a bare position, a bare velocity, and a bare acceleration. Whatever is actually reported up through the hierarchical input levels, the output from muscles can only apply force to the environment. Force affects mass motion, and affects position only by way of its influence on velocity both through F=ma and the effect of force on steady velocity through a viscous medium.
Do you still think that the way “velocity” and “acceleration” affect control is not everyday “prediction”, at least as closely as a controlled “perception” is an everyday perception?