Published online by Cambridge University Press: 01 January 2025
The theoretical and practical importance of a double undertaking is discussed: the development of learning and transfer taxonomies with psychometric relevance and the building of psychometric classificatory systems with implications for learning and instruction. Psychometric classifications of human performances are most often based on the covariation of individual differences. The model presented justifies the expectation that the transfer from learning one task to learning another is linearly dependent on the coefficient of intercorrelation between the two tasks when the coefficient is corrected for attenuation. Two studies so far have explicitly confirmed the main deductions from this model. Contrary to the predictions, however, the regression curves yielded negative intercepts. Two empirically testable explanations are offered, one of which would be in full accordance with the model, while the other would call for a further assumption.
This is a further elaborated version of a paper presented at the 1973 American Psychological Association Convention in Montrèal, Canada. The work has been done under a fellowship by the Swiss National Fund of Scientific Research (Grant SG 86) while the author was an Honorary Fellow at the Wisconsin Research and Development Center for Cognitive Learning at the University of Wisconsin in Madison. Both are gratefully acknowledged. Further thanks is expressed to the members of a former research team that produced the data on which part of the experimental test of the proposed model is based: Rudolf Flühler, Jo Kramis, Urs Murer, and Heinz Stöckli, all at the University of Fribourg in Switzerland. The author also wishes to thank Diane H. Eich and Laurel Gutmann for competent correction and edition of this second-language report.
Parts of the following results were prepared for the diploma-theses of Flühler [1971], Kramis [1971], and Stöchli [1972], together with some analyses not covered here.