Advancements in Joint Modeling of Neural and Behavioral Data

Since the first edition of this book, several new developments have made the joint modeling approach more attractive for researchers in model-based cognitive neuroscience. These developments span several dimensions, such as making joint models more accessible for use in any generic problem, increasing the scalability of the linking structures to prepare them for high-dimensional data, imposing constraints on the temporal dynamics of the model, and instantiating causal relations between neural dynamics and behavioral outcomes. In this chapter, we review many of the new advancements that are now making joint modeling a feasible alternative for relating brain to behavior in realistic settings. In an effort to maintain progress, we also provide an outlook on some of the key problems that remain unsolved.