Modeling Transfer and Learning Effects in SyllogisticReasoning Using Automatically Generated Process Models

Building cognitive process models for reasoning traditionally involve hand-crafting them from underlying theories and assumptions, thereby limiting comparability and transfer between competing theories. To address this, we present a novel approach that automatically constructs process models: Process models are treated as a sequence of cognitive actions/operations that are extracted from cognitive theories and represented in a unified framework while preserving the explanatory merits of the underlying theories. Our method then searches for an optimal sequence to fit the observed reasoning behavior, providing insights into inter-individual differences on the level of reasoning processes. We apply our approach to the domain of syllogistic reasoning and use it to obtain insights into which processes and parts of state-of-the-art models account best for individual reasoning behavior. We use datasets including other reasoning domains as well as the progression of syllogistic reasoning performance over time. This allows us to investigate how processes and components of state-of-the-art models correspond to an individual’s performance in related domains and how they can account for learning effects. Finally, we discuss the potential of our approach for cognitive modeling: First, by utilizing a unified framework for cognitive actions, we can obtain process models combining the best components of state-of-the-art models to account for observed phenomena. Second, it allows us to relate specific processes better to other domains of reasoning, advancing a unified understanding of reasoning. Finally, in-depth insights into the respective theories and models are gained in an objective manner that facilitates the development of cognitive process models.