AutoProp: A Tool to Automate the Construction of Psychological Propositions

Stephen W. Briner, Sacred Heart University
Phillip M. McCarthy, University of Memphis
Danielle S. McNamara, University of Memphis

At the time of publication Stephen Briner was affilitated with University of Memphis.

Abstract

A prototype of an automated tool to construct a propositional textbase, AutoProp, is described and qualitatively assessed. The tool is specifically designed to propositionalize texts for experimental studies that collect and analyze participants’ recall of text. The procedure for creating the propositionalized text is explained, followed by a descriptive analysis of the tool’s propositions as compared to 29 hand-coded propositions. In initial testing, all of AutoProp’s propositions differed from the hand-coded propositions at a superficial level; however, no differences deemed uncorrectable were encountered. Based on the success of these initial results, we conclude that AutoProp is a viable tool worthy of continued examination and development. Limitations of the tool, along with future developmental plans and requirements addressing these limitations are also discussed.