Predictive Modeling of Individual Human Cognition: Upper Bounds and a New Perspective on Performance

Model evaluation is commonly performed by relying on aggregated data as well as relative metrics for model comparison and selection. In light of recent criticism about the prevailing perspectives on cognitive modeling, we investigate models for human syllogistic reasoning in terms of predictive accuracy on individual responses. By contrasting cognitive models with statistical baselines such as random guessing or the most frequently selected response option as well as data-driven neural networks, we obtain information about the progress cognitive modeling could achieve for syllogistic reasoning to date, its remaining potential, and upper bounds of performance future models should strive to exceed. The methods presented in this article are not restricted to the domains of reasoning but generalize to other fields of behavioral research and can serve as useful additions to the modern modeler's toolbox.