A LAMSTAR Network-Based Human Judgment Analysis

Abstract

Judgment analysis (JA) is a technique for modeling and interpreting human judgments that is usually based on multiple linear regression. However, the linear assumptions inherent to this approach can be limiting for modeling both the human judgments and the environmental criterion. This paper addresses this by introducing a formulation of JA based on large memory storage and retrieval (LAMSTAR) artificial neural networks. We describe our LAMSTAR network JA process and use it to analyze data from an air traffic control conflict prediction task. These results are compared with those of a traditional regression-based lens model analysis. We found that the LAMSTAR-based JA did a better job of capturing human judgment, while the regression-based model was more appropriate for the criterion. This suggest that the LAMSTAR-based JA approach has utility when human judgments are not well represented by a linear model. We discuss our results with respect to both the specific application we evaluated as well as meta-analyses of the JA literature. We also explore avenues for future research.