Multiplicatively interacting factors in multinomial processing tree models
Abstract
Schweickert and Chen (2008) discussed systematically how to construct a processing tree using experimental data in which two factors selectively influence two different processes. A more general tree model is discussed in this paper, in which each of the factors can change parameters values at more than one vertex and three or more response classes are allowed. Under certain conditions, a standard processing tree of a simple form suffices. Sufficient and necessary conditions are discussed for such a tree of interest to be applicable. The set of all possible parameter values in such a tree is characterized. Further, the order of processes is discussed. The order can be tested through experimental data for some tree models; in some trees with certain properties, different orders are possible. Similar results are given for generalized rate models with multiplicatively interacting factors (Model 3 of Roberts, 1987). In general, theorems established for the models discussed in this paper are in a more complicated form compared with those in Schweickert and Chen (2008).
Degree
M.S.
Advisors
Schweickert, Purdue University.
Subject Area
Cognitive psychology
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