Linking models and brain measures offers a number of advantages over standard analyses. Models that have been evaluated on previous datasets can provide theoretical constraints and assist in integrating findings across studies. Model-based analyses can be more sensitive and allow for evaluation of hypotheses that would not otherwise be addressable. For example, a cognitive model that is informed from several behavioural studies could be used to examine how multiple cognitive processes unfold across time in the brain. Models can be linked to brain measures in a number of ways. The information flow and constraints can be from model to brain, brain to model, or reciprocal. Likewise, the linkage from model and brain can be univariate or multivariate, as in studies that relate patterns of brain activity with model states. Models have multiple aspects that can be related to different facets of brain activity. This is well illustrated by deep learning models that have multiple layers or representations that can be aligned with different brain regions. Model-based approaches offer a lens on brain data that is complementary to popular multivariate decoding and representational similarity analysis approaches. Indeed, these approaches can realise greater theoretical significance when situated within a model-based approach.
Model-based inference of cognitive processes from unobtrusive gait velocity measurements
