Adaptive Techniques in Multiparameter Problems

This paper describes a technique for conducting multiparameter experiments in a manner such that the number of data points investigated is reduced to a minimum. The method is based upon the observation that human responses to psychophysiological inputs are lawful rather than random, and hence can be predicted from mathematical equations. The procedure is to: (a) collect data on human responses at a few points in the experimental matrix, (b) fit this data with a low-order polynominal, using a computer program to evaluate the coefficients of the equation as a function of the collected data points, and (c) then, using the developed equation, the computer predicts the values that would be observed at other data points. If these computed values are close enough to the observed values at these points, it is assumed that the equation is correct. If the values are not close enough, the new data is entered into the computer and a higher order equation is fitted by a method of least squares. The procedure is iterative, and is continued until the residual error between computed and observed values for all points falls below some desired value. The importance of the technique is that in multiparameter experiments such a technique can reduce the necessary number of observations by several orders of magnitude compared to what would be necessary by conventional techniques.