An Open-Source Relational Network Derivation Script in R for Modeling and Visualizing Complex Behavior for Scientists and Practitioners

Relational models of cognition provide parsimonious and actionable models of generative behavior witnessed in humans. They also inform many current computational analogs of cognition including Deep Neural Networks, Reinforcement Learning algorithms, Self-Organizing Maps, as well as blended architectures that are outperforming traditional semantic models. The black box nature of these computer models artificially limits scientific and applied progress and human computer interaction. This paper presents a first in the field attempt to model relational processes using logical derivation scripts and network graph visualizations written in the open-source R language. These tools are presented as a way for researchers and practitioners to begin to explore more complex relational models in a manner that can advance the theory and empirical science, as well as prepare the field for future collaborations with advanced computational models of cognition.

Automated summary

Relational models of cognition offer actionable models of generative behavior and guidance for computational analogs. However, the black box nature limits scientific and applied progress. This paper presents an attempt to model relational processes using logical derivation scripts and network graph visualizations, providing tools for exploring complex relational models.