Brain sciences are in a difficult position: there is a profusion of theories, an increasing amount of data on brain and behavior, and yet no emerging framework to link them.
One roadblock to how we integrate empirical evidence in scientific theories is that we fit models to data, assuming that these models are correct, and then reason on their ingredients. This methodology can easy lead to circular reasonning. It also encourages idealized experiments on well-controled instanciations of theoretical constructs, such as mental processes, leading to models with little external validity: no ability to conclude on observations outside the given experimental paradigm.
I believe that, for the time being, we need to put less focus on idealized models and strong claims on mental constructs, their validity and organization. Rather, we should focus on models that generalize across many experimental settings, and criticize models more on their predictions than on their ingredients. This agenda has been growing with the use of machine-learning in neuroscience and can lead to more robust empirical evidence. The way forward may lie more on direct fits to behaviour rather than dissociated mental categories, though it requires putting aside short-term promises of a tidy and esthetic model of brain function.
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