Analyzing the Differences in Human Reasoning via Joint Nonnegative Matrix Factorization

AbstractJoint Nonnegative Matrix Factorization (JNMF) is a method for factor analysis that is capable of simultaneously decomposing two datasets into related latent state representations. Enabling factor analysis for contrasting applications, i.e., to find common and distinct structural patterns in data, JNMF has great potential for use in the field of cognitive science. Applied to experimental data, JNMF allows for the extraction of common and distinct patterns of behavior thereby extending the outcomes of traditional correlation-based contrasting methods. In this article, we introduce JNMF to the field of cognitive science and demonstrate its potential on the exemplary domain of syllogistic reasoning by comparing reasoning patterns for different personality factors. Results are interpreted with respect to the theoretical state of the art in syllogistic reasoning research.

Return to previous page