Representing parametric order constraints in multi-trial applications of multinomial processing tree models

Binary multinomial processing tree (MPT) models parameterize the multinomial distribution over a set of <f>J</f> categories, such that each of its parameters, <f>θ1,θ2,…,θS</f>, is functionally independent and free to vary in the interval <f>[0,1]</f>. This paper analyzes binary MPT models subject to parametric order-constraints of the form <f>0⩽θs⩽θt⩽1</f>. Such constraints arise naturally in multi-trial learning and memory paradigms, where some parameters representing cognitive processes would naturally be expected to be non-decreasing over learning trials or non-increasing over forgetting trials. The paper considers the case of one or more, non-overlapping linear orders of parametric constraints. Several ways to reparameterize the model to reflect the constraints are presented, and for each it is shown how to construct a new binary MPT that has the same number of parameters and is statistically equivalent to the original model with the order constraints. The results both extend the mathematical analysis of the MPT class as well as offering an approach to order restricted inference at the level of the entire class. An empirical example of this approach is provided. [Copyright &y& Elsevier]