Abstract

A Multinomial Processing Tree (MPT) is a directed tree with a probability associated with each arc and partitioned terminal vertices. We consider an additional parameter for each arc, a measure such as time. Each vertex represents a process. An arc descending from a vertex represents selection of a process outcome. A source vertex represents processing beginning with stimulus presentation and a terminal vertex represents a response. An experimental factor selectively influences a vertex if changing the factor level changes parameter values on arcs descending from that vertex and no others. Earlier work shows that if each of two factors selectively influences a different vertex in an arbitrary MPT it is equivalent to one of two simple MPTs. Which applies depends on whether the two selectively influenced vertices are ordered by the factors or not. A special case, the Standard Binary Tree for Ordered Processes, arises if the vertices are ordered and the factor selectively influencing the first vertex changes parameter values on only two arcs. We derive necessary and sufficient conditions, testable by bootstrapping, for this case. Parameter values are not unique. We give admissible transformations for them. We calculate degrees of freedom needed for goodness of fit tests.

Highlights

  • A Multinomial Processing Tree (MPT) consists of a finite set of vertices and a set of arcs, each arc being an ordered pair of vertices, such that there is no more than one directed path from one vertex to another, see Figure 1

  • We show that the parameter values are not unique, and show how different sets of values must be related to each other

  • Multinomial Processing Trees are widely used as models of phenomena in psychology

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Summary

Introduction

A Multinomial Processing Tree (MPT) consists of a finite set of vertices and a set of arcs, each arc being an ordered pair of vertices, such that there is no more than one directed path from one vertex to another, see Figure 1. In an MPT model of a list learning, it was found that changing word frequency changed the probability of target recollection, but changed no other parameter [19]. (Note that a path directed from one selectively influenced vertex to the other will not suffice for the vertices to be ordered by the factors, if no arc on the path has a parameter whose value depends on a level of one MMaaththememaatitciscs22002222, ,1100, ,226677. To account for response probabilities, does any binary MPT exist in which word frequency and concreteness selectively influence two different ordered vertices? If there is any MPT that can account for response probabilities and in the MPT word frequency and concreteness selectively influence two ordered vertices, the factors would have to do so in the Standard Binary Tree for Ordered Processes of Figure 1 [23]. We emphasize that if this did not happen, no binary MPT exists in which the two factors selectively influence two ordered vertices

Results
Representation
Admissible Transformations
Degrees of Freedom
Discussion
Full Text
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