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Dr. Jones’ research uses large-scale computational models to better understand the mechanisms that humans use to construct knowledge from statistical experience. In particular, he is interested in understanding the cognitive mechanisms that support acquisition, representation, and degradation of semantic information from naturalistic “big data,” as well as how this knowledge is used in online language comprehension. Dr. Jones’ research has demonstrated that when we consider the real data that humans experience, and at a scale at which humans experience information, much simpler mechanisms of human learning (what he has called “dumb but scalable algorithms”) surprise us by naturally constructing very complex knowledge structures. It is important to consider these systems at scale. More recently, Dr. Jones has explored the integration of computational cognitive models and neuroimaging data, and the translation of cognitive models into automated educational technologies, health informatics, and industrial applications such as password security and information retrieval.