Periodic Reporting for period 3 - UNIFY (A Unified Framework for the Assessment and Application of Cognitive Models) Berichtszeitraum: 2021-01-01 bis 2022-06-30 Zusammenfassung vom Kontext und den Gesamtzielen des Projekts 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 constituentpsychological processes. Because of their unique ability to estimate latent processes, cognitive models areincreasingly applied throughout cognitive neuroscience and clinical psychology. Despite their theoreticalappeal and growing popularity, however, the field of cognitive modeling presents an often bewilderingproliferation of ideas and techniques. Current applications appear idiosyncratic, and the state-of-the-artremains 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” is to provide a unified, systematic treatment of cognitivemodels. By adhering to the basic principles of Bayesian inference we develop new methods andpropose new procedures to address core modeling questions. The innovation takes place both on anabstract level (through the activities of a Quantitative Development Team) and on a concrete, model-specificlevel (through the activities of a Core Applications Team). The model-specific applications –drift decisionmodels, stop-signal race models, reinforcement learning models– were chosen because of their enduringtheoretical impact and their practical relevance for fields such as neuroscience and clinical science.By setting new standards for cognitive modeling we aim to advance a more systematic treatment ofuncertainty and push cognitive model evaluation and application to the next level. A secondary goal is toincrease the availability and boost the impact of the project by making the new procedures available in freesoftware packages such as R and JASP. Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse The primary achievement so far concerns the development and application of two underused but highly promising statistical techniques – bridge sampling and model-averaging. With bridge sampling, researchers can compute a model’s predictive performance in an efficient and reliable manner. With model-averaging, researchers can base their overall conclusion on many models simultaneously: each model’s contribution is combined with that of the others, with its influence weighted with past predictive performance. In addition, considerable progress has been made to make JASP suitable as a general-purpose software program for cognitive models. Specifically, much work has been done on making it easy to add modules, on obtaining the underlying R code, and on developing a module that allows probabilistic programming with the help of a graphical user interface. Behind the scenes, considerable effort has been expended to (1) write course books on Bayesian inference and cognitive modeling; (2) simplify the drift-diffusion model and develop a state-of-the-art routine for its estimation. Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts) As the Core Applications Team starts to unfold its activities, the plan is to create dedicated JASP modules that make it easy to fit the specific models of interest to data and draw conclusions. This also requires that we develop a generic method to place a statistical structure on the model parameters.