Data-Driven Construction of Individualized Process Models for Human Reasoning

Over the past decades, human reasoning research has identified a variety of effects and processes, of which several have been compiled into comprehensive theories. Based on such theories, cognitive models were developed that made the theoretical findings applicable and testable. However, the models often consist of a variety on sub-processes and effects internally, but are not built in a modular way, hindering the transfer of findings between different models and their comparability.We approach this problem by proposing a different perspective: By treating the generation of cognitive process models as a search problem, process models can be derived from cognitive operations automatically in an objective way. Our method is illustrated on the domain of syllogistic reasoning, where we show that it generates a process model that outperforms state-of-the-art models while preserving their explanatory meaning. Finally, we discuss our approach as a framework for streamlining and facilitating cognitive modeling endeavors.