By H. J. Eysenck (auth.), Professor Hans J. Eysenck (eds.)
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Extra info for A Model for Intelligence
Their difficulty is calibrated on some scale using the percentage correct in a standard group as the index of difficulty. From Fig. 2 it can be seen that for a fixed response time, any increase in difficulty will lead to a decrease in proportion correct; as the amount of time allowed is increased, with difficulty held constant, the probability of success will increase. It will also be seen that, like Furneaux (1961), normal ogives are assumed to express relationships between response time and probability of success, and between difficulty and probability of success.
All the above-mentioned features are observable in his editorial comments to a compendium of papers published in 1973 (Eysenck 1973) and will not be detailed here. Instead, attention will be given to a number of issues which support or challenge his views. Brierley (1969), with some justification, comments on the 'static' nature of Eysenck's (1967) 'cube' model of intelligence, initially described in 1953 and 1967. Although serving a different purpose to that of Guilford's structure of intellect cube, it is equally lifeless, and at variance with the complexities of human problem solving, seen for instance in the work of Newell and Simon (1972).
While it is unlikely that many of the problems will be answered in the foreseeable future, some of the more fundamental issues have at least been identified. Butcher (1968) has pointed out that from the point of view of the scientific study of intelligence, what is needed is a law or set of laws which can act as the basis for major advances in our understanding of intelligence. Such laws would, according to Butcher, help establish an acceptable definition of intelligence which would then facilitate further developments.
A Model for Intelligence by H. J. Eysenck (auth.), Professor Hans J. Eysenck (eds.)