Data-Driven Construction of Individualized Process Models for Human Reasoning

Over the past decades, cognitive science research of human reasoning has identified a variety of effects and processes, of which several have been compiled into comprehensive theories of human reasoning. Based on such theories, cognitive models were developed that made the theoretical findings applicable and testable.However, most of those models still suffer from two major drawbacks: First, they are commonly developed on an aggregate level and are rarely applied to individuals. Second, the models often consist of a variety on sub-processes and effects internally, but are not build in a modular way, hindering the transfer of findings between different models. We present a data-driven methodology that generates modular process models from such sub-processes and effects, exemplarily applied to the domain of syllogistic reasoning. Given the question-answer pattern of reasoners, a combination of sub-processes maximizing performance is calculated, which outperforms the current state of the art in terms of the ability to account for individual reasoning behavior.