The Ontogenesis of Purposive Activity
People and animals have a remarkable ability to develop skills from experience. Ways of studying this are discussed, with particular reference to its imitation in artifacts, which may sometimes be regarded as embodying hypotheses about the working of the nervous system. It is suggested that thought processes depending on language are less important than is often suggested. Possible constituent elements and fundamental operations of a nervous system are briefly reviewed. Some principles of very general applicability are treated, particularly the reduction of signal redundancy and the use of a principle termed “significance feedback” to control adaptive changes in a self-organizing system.
People and animals are able to survive in environments containing threats such as predators and precipices, and in which skilful activity is needed to win food and other essentials. This capability depends on complex information processing by their nervous systems, which must store a large amount of information to specify the nature of the processing carried out. Some of the stored information is genetically determined and some is ontogenetic, or resulting from previous experience of the individual. In experimental psychology the attempt is often made to determine the relative extents to which a skill is inherited and learned. The answer is seldom clear-cut, since the two kinds of information are not held in separate watertight compartments. Sometimes an indication of their relative importance can be obtained, however, as when animals are tested for a skill after being reared in an impoverished environment. An example of such a study has been reported (7) which will be discussed later.
Some of the information acquired by experience takes the form of memories of discrete events, at least in the case of human beings (see (11)). The genetically-determined information does not contain recollections of particular events; it is rather in the nature of skills, or policies needed to determine outputs as functions of input signals. Much of the ontogenetic information is also of the “skill” or “policy” kind, even though it results from a succession of discrete events in the individual’s experience. For example, a driver of a car approaching a bend does not normally begin there and then, to reflect on the previous occasions on which he approached similar bends, and to process a mass of stored information on how he reacted and what was the outcome each time. Instead he uses information of the “skill” type derived from the past events.
The ability of people and animals to acquire skills by experience is well beyond anything so far achieved in artifacts. It is only necessary to refer to the fact that people learn to read cursive handwriting of poor quality, and people and animals to ride monocycles, to illustrate this. There is therefore much interest in trying to devise “learning machines” with some of the same capabilities. Such machines could have direct practical utility and some aspects of their operation might constitute useful hypotheses about the operation of nervous systems.
KeywordsModel Neuron Significance Feedback Cochlear Nucleus Threshold Element Redundancy Reduction
Unable to display preview. Download preview PDF.
- 1.Andrew, A.M.: Significance feedback in neural nets. Report of Biol. Comptr. Labty., Univ. of Illinois, Urbana (1965).Google Scholar
- 2.Andrew, A.M.: “Progress in Cybernetics”, ed. J. Rose, p. 359. London: Gordon and Breach: 1970.Google Scholar
- 3.Barlow, H.B.: “Mechanisation of Thought Processes”, p. 535. London: H.M.S.O.: 1959.Google Scholar
- 4.Barlow, H.B.: “Sensory Communication”, ed. W.A. Rosenblith, p. 217, New York: M.I.T. Press and Wiley (1961).Google Scholar
- 5.Beurle, R.L.: “Principles of Self-Organization”, ed. H. Von Foerster and G.W. Zopf, p. 291, Pergamon Press (1962).Google Scholar
- 6.Bruner, J.S., Goodnow, J.J. and Austin, G.A.: A Study of Thinking, New York: Wiley (1956).Google Scholar
- 8.Braines, S.N., Napalkov, A.V. and Svechinski, V.B.: Problems of Neurocybernetics. Moscow: Academy of Med. Sci. (Translation NLL/24/4/BRAI from Natl. Lending Library for Science and Tech., England, or OTS: 60-41, 639 from U.S. Bureau of Commerce) (1959).Google Scholar
- 9.Dreyfus, H.L.: Alchemy and artificial intelligence. RAND Corp. report P 3244.Google Scholar
- 10.Gabor, D., Wilby, W.P.L. and Woodcock, R.: Proc. I.E.E., London, Pt. B., 108, p. 422 (1961).Google Scholar
- 12.Hebb, D.O.: Organization of behavior, New York: Wiley (1949).Google Scholar
- 14.Hunt, E.B., Marin, J. and Stone, P.J.: Experiments in Induction. New York: Academic Press (1966).Google Scholar
- 15.Kogan, A.B.: Scientia, 103, Sept.-Oct. issue, p. 1 (1968).Google Scholar
- 16.Minsky, M. and Papert, S.: Perceptrons. Cambridge, Mass.: M. I.T. Press (1969).Google Scholar
- 17.Rosenblatt, F.: Principles of Neurodynamics. Washington: Spartan Books (1962).Google Scholar
- 18.Samuel, A.L.: I.B.M. Jnt. Res. and Dev. 2, p. 320 (1959).Google Scholar
- 19.Sinclair-de-Zwart, H.: “Studies in Cognitive Development”, ed. D. Elkind and J.H. Flavell, p. 315, New York: O.U.P. (1969).Google Scholar
- 20.Stafford, R. (Ed.): “Biophysics and Cybernetic Systems”, In: M. Maxfield, A. Callahan and L. J. Fogel, p. 81, Washington: Spartan Books (1965).Google Scholar
- 21.Tomovic, R.: “Sensitivity Analysis of Dynamic Systems”. New York: McGraw Hill (1963).Google Scholar
- 24.Winograd, S. and Cowan, J.: “Reliable Computation in the Presence of Noise”, Cambridge, Mass.: M.I.T. Press (1963).Google Scholar