The Generalization Function Is Determined by One Subject's Probability Learning Data USPHS

Author(s):

Levine, Michael V.

Publication Year:

1974

Report Number:

RB-74-38

Source:

ETS Research Bulletin

Document Type:

Report

Page Count:

28

Subject/Key Words:

National Science Foundation (NSF), United States Public Health Service (USPHS), United States Air Force Office of Scientific Research, Data Analysis, Generalization, Learning Theories, Mathematical Models, Prediction

Abstract

In his recent review of probability learning, Estes (1972, page 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 This paper describes, gives the rationale and proves the validity of the previously unpublished technique. (28pp.)

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• http://dx.doi.org/10.1002/j.2333-8504.1974.tb00666.x