Analysis of pattern recognition by man using detection experiments

Abstract

This paper addresses the problem of analyzing biological pattern recognition systems. As no complete analysis is possible due to limited observability, the theoretical part of the paper examines some principles of construction for recognition systems. The relations between measurable and characteristic variables of these systems are described. The results of the study are:

• 1. Human recognition systems can always be described by a model consisting of an analyzer (F _A) and a linear classifier.

• 2. The linearity of the classifier places no limits on the universal validity of the model. The principle of organization of such a system may be put into effect in many different ways.

• 3. The analyzer function F _A determines the transformation of external patterns into their internal representations. For the experiments described in this paper, F _A can be approximated by a filtering operation and a transformation of features (contour line filter).

• 4. Narrow band filtering (comb filter) in the space frequency domain is inadequate for pattern recognition because noise of different bandwidths and mean frequencies affects sinusoidal gratings differently. This excludes the use of a Fourier analyzer.

• 5. The relations between the measurable variables, which are the probabilities of detection (P _D curves), and the characteristic variables of the recognition system are established analytically.

• 6. The probability of detection not only depends on signal energy but also on signal structure. This would not be the case in a simple matched filter system.

• 7. The differing probabilities of error in multiple detection experiments show that the interference is pattern specific and the bandwidth (steepness of the P _D curves) is different for the different sets of patterns.

• 8. The distance between the reference vectors in feature space can be determined from the internal representation of the patterns defined by the model. Through multiple detection experiments it is possible to determine not only the relative distances between the patterns but also their absolute position in feature space.