LEONARD UHR M ental Health Research Institute
The University of Michigan
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The perceptual mechanisms of living organisms have developed around wavebands of energy that are commonly emitted by objects in our physi-cal world: the eye around vibrations of subatomic particles, the ear around vibrations of molecules. The purpose of perception is to reduce the signals that the mechanism senses-that is, this energy as it would affect a typical physical object like a photographic plate or a sounding board, and to judge whether it belongs to any of a class of signs that are of interest to the organism because they suggest acts that it should take.
The judgment that some part of the flow of experience belongs to such a class is the act of "pattern recognition." Thus pattern recognition is the decision-making process that assigns to some experiences (carved by this very decision out of the total flow of experience) some internal meanings.
(For the moment, by "meaning" I simply mean some set of connections.) A bit more formally, pattern recognition is a many-one mapping from a very large set of arrays to a relatively much smaller set of names. The word "mapping" should be taken in an intuitive rather than a mathemati-cal sense, for it simply indicates that some set of transformations has to be made to get from the raw input data in the array to the choice of a name.
If we had nice mappings, there would be no pattern-recognition problems.
Since we are talking about inputs from a physical world, we are always talking about arrays that contain discrete sets of data. This is so because the primitive quanta in the physical world are discrete and because any sensing mechanism has a maximal resolving power (uncertainty at the level of physics, where we resolve objects with objects of their own size;
the "jnd" -just noticeable difference-at the level of psychology).
The need for and value of pattern recognition comes about only when some economy is effected by the recognition process. Such economizing does in fact take place in most real situations, where the objects, whose
*This paper is an edited portion of a manuscript in progress, tentatively titled "Computers and Discovery." It was prepared for presentation at the 6th Annual IBM Conference on Bio-medical Electronics in Poughkeepsie, Oct. 6, 1964, and at the Conference on Informa-tion Handling in Pittsburgh, Oct. 7, 1964. PreparaInforma-tion of this paper, and the author's personal research discussed therein, was partially supported under NIH grant M5254 and ARPA contract SD 266.
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energy emission must be recognized, themselves are affected by coherent forces that bend, stretch or otherwise deform them. And, of course, the position of the object vis-a-vis the observer leads to the whole set of linear transformations (as they change their positions in three-dimensional space). Since the organism's problem is to continue to recognize an object as itself, even though it has undergone some linear transformation or nonredundant codes of the sort frequently seen in man-made information processors. The operation and address codes in computers are good examples. A worse example is the arbitrary, random assignment of each possible array to a name. Because there is no simplifying set of trans-formations that will turn one member of the set of arrays with a par-ticular name into the other members, each array would have to be identi-fied completely as itself, in effect named as itself, and then a table would have to be used to assign the class name.
The word "perception" would seem to be somewhat broader than the word "pattern recognition," since the former refers to the entire process of transforming the raw data of the stimulus into the recognition, the attribution of a class name. But there is certainly great overlap between these two words as they occur in common usage. Perception tends to emphasize the earlier transformations that regularize the input data, making the different examples of the same pattern in some sense more similar to one another. Pattern recognition tends to emphasize the final step when the instance is given a name.
I will use the words "input," "sensed data," "instance," and "array"
more or less synonymously for the material presented to a pattern recog-nizer; "measurement," "characterizer," "transformation," "operator,"
and "mapping" for the steps that the pattern recognizer takes; and "pat-tern," "name," "class," and "output" for the result. At times, distinc-tions between these near-synonyms will be noted, but their similarity would seem to be the salient feature about them.
The large body of pattern-recognition research that has arisen in the past ten years in the interdisciplinary area between psychology, psychia-try, mathematics, engineering, and physiology that is variously called
"cybernetics," "artificial intelligence," "systems sciences," "communica-tion sciences," and "informa"communica-tion-processing sciences," among other names, has been largely concerned with a particular simplified version of the general problem of perception and pattern recognition. This has been
PATTERN RECOGNITION 53 the problem of the assigning of the appropriate class name to an isolated two-dimensional array of discrete symbols. The bulk of the research has been on recognition of letters of the alphabet and, occasionally, other visual patterns. Most of the rest of the work has been on the recognition of spoken words or phonemes. A scattering of work has investigated recognition of other arrays, such as Morse code and diagnostic symp-toms. A good bit of research that has gone on under other names, such as "concept formation," "language processing," "learning," "memoriz-ing," "prediction," and "decision-making" is closely related and, in fact, often investigates the same problems.
Virtually all of this research handles the problem of naming a static, isolated matrix whose primitive symbols are discrete and clearly dis-criminable. The primitive set of symbols usually contains only the two values black and white (or 0 and 1) in the case of visual patterns, or a small range of intensities (typically from 0 through 7) in the case of audi-tory patterns. A primitive symbol will, then, reflect the amount of light at a given spot in a two-dimensional picture, or the amount of sound energy of a given frequency at a given time. When I say that each primitive sym-bol can be perfectly resolved, I am talking about things that are often very tiny, of the magnitude of the individual spots on a TV raster or the amount of light that subtends a single cone in the retina. Thus there might well be thousands of such spots in the single pattern to be recognized, even if this pattern were merely a simple curve.
Psychology has amassed a great number of particular facts as to the interactions of the many factors involved in even the simplest perceptual acts. But it has not developed anything in the way of a coherent theory of how the crucial recognition toward which the entire perceptual process leads actually takes place. We are variously told that the brain compares its ideas with the incoming percepts, that the percept calls forth the memory trace, that the brain recreates the pattern until there is no more mismatch, and that this process is the idea, and so on. But what do words like "compare," "idea," "trace," or "recreate" signify?
But we now have a large number of computer programs and analog computers (and remember, these are equivalent, simply being alternate methods of representation) that do in fact recognize patterns. For want of anything that we could seriously call scientific theory, that was more than suggestive verbiage, these programs must be taken seriously as the first attempts toward developing a good theory. For they are, in fact, theoretical models of the traditional sort. They may well be bad models, in that they are inelegant, without great power, or (but this is the case surprisingly infrequently) contraverted by the empirical data. But bad theories, with their power to make things clear and lead to their own downfall, are far better than no theories at all.