The generalization function is determined by one subject's probability learning data

In his recent review of probability learning, Estes (1972, p. 96) notes that “the chief limitation on effective application” of the models for learning with a continuum of responses to have emerged from the linear and pattern learning models is the failure to specify the smearing or generalization function. He notes that “a useful technique has been developed by Levine for estimating this function from individual data.” This paper describes, gives the rationale, and proves the validity of the previously unpublished technique.