In a Scientific American blog post Deep thought is dead, Long live deep thought, a bioinformatics analyst broods on the question, ‘Where are these jobs that will require such rapid “searching, browsing, assessing quality, and synthesizing the vast quantities of information?" and decides quiet a lot of information can be gained by this type of superficial processing of large quantities of material.
"Our ability to produce data is outstripping our ability to understand
it. In fact, the need to make sense of these mountains of information is
so great that it’s given rise to one of the hottest interdisciplinary fields on the market: data mining and predictive analytics."
Perhaps it's a trade-off. A lot can be gained from slowly and deeply reading a dense but wise text, but a different sort of knowledge (and equally legitimate) can be arrived at by superficial processing of large quantities of material. This more superficial processing may be particularly well suited to inductive problems where principles may be extrapolated from different examples or instances.
researchers in Neuroimage found that the striatal-thalamic regions (blue left) were important for the extrapolation step in inductive problem solving. This is all very interesting because of the association of striatal structures with curiosity and novelty.
One wonders whether strong caudate learners should be considered as a distinct learning style - novel, curiosity driven, inductive learners who learn best by engaging primary or direct experiences- then reasoning back to first principles.
We see many of these types of learners in high tech / computer engineering fields - and that probably also jives with the video gamers have bigger brains (caudates) research.