Universalistic Theories

WORK/CAREER/OCCUPATION, THEORIES OF.

UNIVERSAL LAW OF GENERALIZATION. The American psychologist/cognitive scientist Roger N. Shepard (1929- ) proposed a universal law of generalization for psychological science that attempts to advance a principle in psychology that is comparable in generality to the English physicist and mathematician Sir Isaac Newton's (16421727) universal law of gravitation in physics. The new law is based on the assumption that because any object or situation experienced by an individual is unlikely to recur in exactly the same form and context, psychology's first general law should be a law of generalization. Historically, learning theorists supposed that a principle of conditioning (via the mechanisms of reinforcement and/or contiguity) could be the primary principle, and where what is learned then generalizes to new situations (left open for later formulation) could be a secondary principle. Over 2,000 years ago, the Greek philosopher Aristotle (384-322 B.C.) recognized - via his principle of association by resemblance - that similarity is fundamental to mental processes, but it was not until the beginning and middle of the 20th century that experimental investigations were conducted on the issue of generalization/similarity of stimuli - first by Ivan Pavlov in the 1920s; then by Norman Guttman, H. I. Kalish, and Roger N. Shepard in the 1950s; cf., Mostofsky (1965). Shepard suggests that humans generalize from one situation to another not because they cannot tell the difference between the two situations, but because they judge that the situations are likely to belong to a set of situations having the same consequences. Generalization - that arises from uncertainty about the distribution of consequential stimuli in

"psychological space" - is to be distinguished from failure of discrimination - that arises from uncertainty about the relative locations of individual stimuli in that space. Accordingly, in his universal law of generalization for psychological science, Shepard posits the notion of a "psychological space" for any set of stimuli by determining metric distances between the stimuli such that the probability that a response learned to any stimulus will generalize to any other is an invariant monotonic function of the distance between them. This probability of generalization, to a good approximation, decays exponentially with this distance, and does so in accordance with one of two metrics, depending on the relation between the dimensions along which the stimuli vary. Shepard asserts that these empirical regularities are mathematically derivable from universal principles of natural phenomena and probabilistic geometry that may - via evolutionary internalization - tend to govern the behaviors of all sentient organisms. Shepard suggests that psychological science undoubtedly has lagged behind physical science by at least 300 years and, just as likely, predictions of behavior may never attain the precision for animate bodies/entities that it has for celestial bodies. However, psychology inherently may not be limited merely to the descriptive characterization of the behaviors of particular terrestrial species, but possibly - behind the diverse behaviors of humans and animals, as behind the various motions of planets and stars - one may eventually discern the operation of universal laws. See also ASSOCIATION, LAWS/PRINCIPLES OF; GENERALIZATION, PRINCIPLES OF. REFERENCES

Newton, I. (1687). Philosophiae naturalis principia mathematica. London: Royal Society. Mostofsky, D. I. (Ed.) (1965). Stimulus generalization. Stanford, CA: Stanford University Press. Shepard, R. N. (1987). Toward a universal law of generalization for psychological science. Science, 237, 13171323.

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