A tutorial on joint models of neural and behavioral measures of cognition

Publication date: June 2018Source: Journal of Mathematical Psychology, Volume 84Author(s): James J. Palestro, Giwon Bahg, Per B. Sederberg, Zhong-Lin Lu, Mark Steyvers, Brandon M. TurnerAbstractA growing synergy between the fields of cognitive neuroscience and mathematical psychology has sparked the development of several unique statistical approaches exploiting the benefits of both disciplines (Turner, Forstmann et al., 2017). One approach in particular, called joint modeling, attempts to model the covariation between the parameters of “submodels” intended to capture important patterns in each stream of data. Joint models present an interesting opportunity to transcend conventional levels of analyses (e.g., Marr’s hierarchy; Marr, 1982) by providing fully integrative models (Love, 2015). In this manuscript, we provide a tutorial of two flavors of joint models — the Directed and Covariance approaches. Computational procedures have been developed to apply these approaches to a number of cognitive tasks, yet neither have been made accessible to a wider audience. Here, we provide a step-by-step walkthrough on how to develop submodels of each stream of data, as well as how to link the important model parameters to form one cohesive model. For convenience, we provide code that uses the Just Another Gibbs Sampler (Plummer, 2003) software to perform estimation of the model parameters. We close with a demonstration of the approach applied to actual data from a contrast disc...
Source: Journal of Mathematical Psychology - Category: Psychiatry & Psychology Source Type: research