Cognitive models formalize substantive theory about how people reason, learn, decide, and act.
Cognitive models also serve as measurement tools that explain observed behavior in terms of constituent
psychological processes. Because of their unique ability to estimate latent processes, cognitive models are
increasingly applied throughout cognitive neuroscience and clinical psychology. Despite their theoretical
appeal and growing popularity, however, the field of cognitive modeling presents an often bewildering
proliferation of ideas and techniques. Current applications appear idiosyncratic, and the state-of-the-art
remains unclear. This lack of systematicity makes it difficult for researchers and practitioners to develop,
understand, and apply important cognitive models.
The main goal of the Advanced ERC project “UNIFY” was to provide a unified, systematic treatment of cognitive
models. By adhering to the basic principles of Bayesian inference we developed new methods and
proposed new procedures to address core modeling questions. The innovation took place both on an
abstract level (through the activities of a Quantitative Development Team) and on a concrete, model-specific
level (through the activities of a Core Applications Team).
In the UNIFY project we set new standards for cognitive modeling. By advancing a more systematic treatment of
uncertainty we aimed to push cognitive model evaluation and application to the next level. A secondary goal was to
increase the availability and boost the impact of the project by making the new procedures available in the free
software packages R and JASP. Primary new technology was developed to test models and quantify the
associated uncertainty. The project also revealed that experts often disagree on the preferred modeling approach,
underscoring the need to apply multiple models or multiple analysis teams.
