Computational Frameworks for Theory Building in Psychology

Mar 24 14:00 ₂₀₂₆

Abstract: Psychological theories are often incompletely specified—that is, certain cases are overlooked, and some statements are ambiguous or imprecise. This is largely due to the fact that such theories are rarely expressed using formal models. Yet in many scientific disciplines, formal modeling plays a crucial role in theory-building: it enhances the precision of predictions, strengthens scientific reasoning, and facilitates collaboration. We propose two automata-based frameworks and a methodology for modeling psychological theories—that is, for transforming informal, verbal descriptions into formal, analyzable models.

These frameworks use communicating finite automata, along with a refinement procedure for theory-building, as well as model-checking to verify that a model is faithful to the original theory. We prove the pseudo-inclusion of traces of the refinement procedure, as well as a completeness property of our framework.

This method highlights theoretical ambiguities and inconsistencies, and prompts research questions. We illustrate our method using Lazarus and Folkman’s theory of stress as well as the consciousness theory called the Global Neuronal Workspace hypothesis. We provide a step-by- step formalization of these theories.